#load("vcomball20210902.Rda")
load(path(here::here("InitalDataCleaning/Data/vcomball20210902.Rda")))
d <- vcomball
# load("vsurvall20210902.Rda")
# d <- vsurvall

#load("vsiteid20210601.Rda")
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
new.d.1 <- data.frame(matrix(ncol=0, nrow=nrow(d)))

SITE ID

  • Codes(based on Surveyid)
    • 10 Greater CA
    • 20 Georgia
    • 25 North Carolina
    • 30 Northern CA
    • 40 Louisiana
    • 50 New Jersey
    • 60 Detroit
    • 61 Michigan
    • 70 Texas
    • 80 Los Angeles County
    • 81 USC-Other
    • 82 USC-MEC
    • 90 New York
    • 94 Florida
    • 95 WebRecruit-Limbo
    • 99 WebRecruit
  siteid <- as.factor(trimws(d[,"siteid"]))
  #new.d.n <- data.frame(new.d.n, siteid) # keep NAACCR coding
  
  levels(siteid)[levels(siteid)=="80"] <- "Los Angeles County.80"
  levels(siteid)[levels(siteid)=="30"] <- "Northern CA.30"
  levels(siteid)[levels(siteid)=="10"] <- "Greater CA.10"
  levels(siteid)[levels(siteid)=="60"] <- "Detroit.60"
  levels(siteid)[levels(siteid)=="40"] <- "Louisiana.40"
  levels(siteid)[levels(siteid)=="20"] <- "Georgia.20"
  levels(siteid)[levels(siteid)=="61"] <- "Michigan.61"
  levels(siteid)[levels(siteid)=="50"] <- "New Jersey.50"
  levels(siteid)[levels(siteid)=="70"] <- "Texas.70"
  levels(siteid)[levels(siteid)=="99"] <- "WebRecruit.99"
  levels(siteid)[levels(siteid)=="21"] <- "Georgia.21"
  levels(siteid)[levels(siteid)=="81"] <- "USC Other.81"
  levels(siteid)[levels(siteid)=="82"] <- "USC MEC.82"

  siteid_new<- siteid
  d<-data.frame(d, siteid_new)
  new.d <- data.frame(new.d, siteid)
  new.d <- apply_labels(new.d, siteid = "Site ID")
  new.d.1 <- data.frame(new.d.1, siteid)
  siteid_count<-count(new.d$siteid)
  colnames(siteid_count)<- c("Registry", "Total")
  kable(siteid_count, format = "simple", align = 'l', caption = "Overview of all Registries")
d<-d[which(d$siteid_new == params$site),]
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
#new.d<-new.d[which(new.d$siteid == params$site),]

SURVEY ID

  • Scantron assigned SurveyID
  surveyid <- as.factor(d[,"surveyid"])
  isDup <- duplicated(surveyid)
  numDups <- sum(isDup)
  dups <- surveyid[isDup]
  
  new.d <- data.frame(new.d, surveyid)
  new.d <- apply_labels(new.d, surveyid = "Survey ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 0"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
## factor(0)
## 1575 Levels: 200014  200016  200017  200018  200024  200025  200027  200028  200031  200036  200038  200041  ... 211878
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$surveyid)))
## [1] 0

LOCATION NAME

  • Name of Registry delivery location
  locationname <- as.factor(d[,"locationname"])
  
  new.d <- data.frame(new.d, locationname)
  new.d <- apply_labels(new.d, locationname = "Recruitment Location")
  temp.d <- data.frame (new.d, locationname)

  result<-questionr::freq(temp.d$locationname, total = TRUE)
  #Create a NICE table
  kable(result, format = "simple", align = 'l', caption = "Overview of Registry delivery location")
Overview of Registry delivery location
n % val%
Georgia 1575 100 100
Total 1575 100 100

RESPOND ID

  • From Barcode label put on last page of survey by registries, identifies participant. ResponseID is assigned by the registries.
  respondid <- as.factor(d[,"respondid"])
  #remove NAs in respondid in order to avoid showing NAs in duplicated values
  respondid_rm<-respondid[!is.na(respondid)]
  isDup <- duplicated(respondid_rm)
  numDups <- sum(isDup)
  dups <- respondid_rm[isDup]
  
  new.d <- data.frame(new.d, respondid)
  new.d <- apply_labels(new.d, respondid = "RESPOND ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 2"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
## [1] 20100647 20100647
## 1573 Levels: 20100005 20100010 20100014 20100018 20100021 20100022 20100024 20100030 20100032 20100033 ... 20800065
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$respondid)))
## [1] 0

METHODOLOGY

  • How survey was completed
    • P=Paper
    • O=Online complete
st_css()
  methodology <- as.factor(d[,"methodology"])
  levels(methodology) <- list(Paper="P",
                              Online="O")
  methodology <- ordered(methodology, c("Paper", "Online"))
  new.d <- data.frame(new.d, methodology)
  new.d <- apply_labels(new.d, methodology = "Methodology for Survey Completion")
  temp.d <- data.frame (new.d, methodology)  
  
  result<-questionr::freq(temp.d$methodology, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Paper 1575 100 100
Online 0 0 0
Total 1575 100 100

A1: Date of diagnosis

  • A1. In what month and year were you first diagnosed with prostate cancer?
# a1month
a1month <- as.factor(d[,"a1month"])
  
  new.d <- data.frame(new.d, a1month)
  new.d <- apply_labels(new.d, a1month = "Month Diagnosed")
  temp.d <- data.frame (new.d, a1month) 
  
  result<-questionr::freq(temp.d$a1month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")
A1:month diagnosed
n % val%
0* 1 0.1 0.1
1 77 4.9 5.8
10 108 6.9 8.1
11 100 6.3 7.5
12 75 4.8 5.7
2 93 5.9 7.0
25 1 0.1 0.1
3 117 7.4 8.8
4 117 7.4 8.8
48 1 0.1 0.1
5 124 7.9 9.4
6 180 11.4 13.6
7 118 7.5 8.9
8 113 7.2 8.5
9 101 6.4 7.6
NA 249 15.8 NA
Total 1575 100.0 100.0 
  #count<-as.data.frame(table(new.d$a1month))
  #colnames(count)<- c("a1month", "Total")
  #freq1<-table(new.d$a1month)
  #freq<-as.data.frame(round(prop.table(freq1),3))
  #colnames(freq)<- c("a1month", "Freq")
  #result<-merge(count, freq,by="a1month",sort=F)
  #kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")

#a1year
  tmp<-d[,"a1year"]
  tmp[tmp=="15"]<-"2015"
  a1year <- as.factor(tmp)
  #levels(a1year)[levels(a1year)=="15"] <- "2015"
  #a1year[a1year=="15"] <- "2015"  # change "15" to "2015"
  #a1year <- as.Date(a1year, format = "%Y")
  #a1year <- relevel(a1year, ref="1914")

  new.d <- data.frame(new.d, a1year)
  new.d <- apply_labels(new.d, a1year = "Year Diagnosed")
  temp.d <- data.frame (new.d, a1year) 

  result<-questionr::freq(temp.d$a1year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:year diagnosed")
A1:year diagnosed
n % val%
1916 5 0.3 0.4
1917 4 0.3 0.3
1918 1 0.1 0.1
1941 1 0.1 0.1
1943 1 0.1 0.1
1944 1 0.1 0.1
1945 1 0.1 0.1
1947 1 0.1 0.1
1948 1 0.1 0.1
1949 2 0.1 0.1
1951 1 0.1 0.1
1952 3 0.2 0.2
1953 1 0.1 0.1
1954 2 0.1 0.1
1955 1 0.1 0.1
1956 2 0.1 0.1
1987 1 0.1 0.1
1989 1 0.1 0.1
1990 1 0.1 0.1
1993 3 0.2 0.2
1995 2 0.1 0.1
1996 1 0.1 0.1
1997 1 0.1 0.1
1998 2 0.1 0.1
1999 3 0.2 0.2
2000 1 0.1 0.1
2001 1 0.1 0.1
2003 1 0.1 0.1
2004 3 0.2 0.2
2005 3 0.2 0.2
2006 3 0.2 0.2
2007 2 0.1 0.1
2008 4 0.3 0.3
2009 5 0.3 0.4
2010 7 0.4 0.5
2011 5 0.3 0.4
2012 12 0.8 0.9
2013 19 1.2 1.4
2014 71 4.5 5.2
2015 369 23.4 26.8
2016 462 29.3 33.5
2017 240 15.2 17.4
2018 86 5.5 6.2
2019 24 1.5 1.7
2020 15 1.0 1.1
2021 2 0.1 0.1
NA 197 12.5 NA
Total 1575 100.0 100.0 
  #a1not
# 1=I have NEVER had prostate cancer
# 2=I HAVE or HAVE HAD prostate cancer
# (paper survey only had a bubble for “never had” so value set to 2 if bubble not marked)"
  a1not <- as.factor(d[,"a1not"])
  levels(a1not) <- list(NEVER_had_ProstateCancer="1",
                         HAVE_had_ProstateCancer="2")
  new.d <- data.frame(new.d, a1not)
  new.d <- apply_labels(new.d, a1not = "Not Diagnosed")
  temp.d <- data.frame (new.d, a1not) 

  result<-questionr::freq(temp.d$a1not, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:not diagnosed")
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 5 0.3 0.3
HAVE_had_ProstateCancer 1570 99.7 99.7
Total 1575 100.0 100.0

A2: Identify as AA

  • A2. Do you identify as Black or African American?
    • 2=Yes
    • 1=No
a2 <- as.factor(d[,"a2"])
# Make "*" to NA
a2[which(a2=="*")]<-"NA"
levels(a2) <- list(No="1",
                   Yes="2")
  a2 <- ordered(a2, c("Yes","No"))
  
  new.d <- data.frame(new.d, a2)
  new.d <- apply_labels(new.d, a2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, a2) 
  
  result<-questionr::freq(temp.d$a2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A2")
A2
n % val%
Yes 1376 87.4 99.9
No 2 0.1 0.1
NA 197 12.5 NA
Total 1575 100.0 100.0

A3: Black or African American group

  • A3. If Yes: A2. Which Black or African American group(s) and other races/ethnicities do you identify with? Mark all that apply.
    • A3_1: 1=Black/African American
    • A3_2: 1=Nigerian
    • A3_3: 1=Jamaican
    • A3_4: 1=Ethiopian
    • A3_5: 1=Haitian
    • A3_6: 1=Somali
    • a3_7: 1=Guyanese
    • A3_8: 1=Creole
    • A3_9: 1=West Indian
    • A3_10: 1=Caribbean
    • A3_11: 1=White
    • A3_12: 1=Asian/Asian American
    • A3_13: 1=Native American or American Indian or Alaskan Native
    • A3_14: 1=Middle Eastern or North African
    • A3_15: 1=Native Hawaiian or Pacific Islander
    • A3_16: 1=Hispanic
    • A3_17: 1=Latino
    • A3_18: 1=Spanish
    • A3_19: 1=Mexican/Mexican American
    • A3_20: 1=Salvadoran
    • A3_21: 1=Puerto Rican
    • A3_22: 1=Dominican
    • A3_23: 1=Columbian
    • A3_24: 1=Other
a3_1 <- as.factor(d[,"a3_1"])
  levels(a3_1) <- list(Black_African_American="1")
  new.d <- data.frame(new.d, a3_1)
  new.d <- apply_labels(new.d, a3_1 = "Black_African_American")
  temp.d <- data.frame (new.d, a3_1)
  result<-questionr::freq(temp.d$a3_1, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Black_African_American")
1. Black_African_American
n % val%
Black_African_American 1451 92.1 100
NA 124 7.9 NA
Total 1575 100.0 100 
a3_2 <- as.factor(d[,"a3_2"])
  levels(a3_2) <- list(Nigerian="1")
  new.d <- data.frame(new.d, a3_2)
  new.d <- apply_labels(new.d, a3_2 = "Nigerian")
  temp.d <- data.frame (new.d, a3_2)
  result<-questionr::freq(temp.d$a3_2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Nigerian")
2. Nigerian
n % val%
Nigerian 25 1.6 100
NA 1550 98.4 NA
Total 1575 100.0 100 
a3_3 <- as.factor(d[,"a3_3"])
  levels(a3_3) <- list(Jamaican="1")
  new.d <- data.frame(new.d, a3_3)
  new.d <- apply_labels(new.d, a3_3 = "Jamaican")
  temp.d <- data.frame (new.d, a3_3)
  result<-questionr::freq(temp.d$a3_3, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Jamaican")
3. Jamaican
n % val%
Jamaican 42 2.7 100
NA 1533 97.3 NA
Total 1575 100.0 100 
a3_4 <- as.factor(d[,"a3_4"])
  levels(a3_4) <- list(Ethiopian="1")
  new.d <- data.frame(new.d, a3_4)
  new.d <- apply_labels(new.d, a3_4 = "Ethiopian")
  temp.d <- data.frame (new.d, a3_4)
  result<-questionr::freq(temp.d$a3_4, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Ethiopian")
4. Ethiopian
n % val%
Ethiopian 3 0.2 100
NA 1572 99.8 NA
Total 1575 100.0 100 
a3_5 <- as.factor(d[,"a3_5"])
  levels(a3_5) <- list(Haitian="1")
  new.d <- data.frame(new.d, a3_5)
  new.d <- apply_labels(new.d, a3_5 = "Haitian")
  temp.d <- data.frame (new.d, a3_5)
  result<-questionr::freq(temp.d$a3_5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Haitian")
5. Haitian
n % val%
Haitian 7 0.4 100
NA 1568 99.6 NA
Total 1575 100.0 100 
a3_6 <- as.factor(d[,"a3_6"])
  levels(a3_6) <- list(Somali="1")
  new.d <- data.frame(new.d, a3_6)
  new.d <- apply_labels(new.d, a3_6 = "Somali")
  temp.d <- data.frame (new.d, a3_6)
  result<-questionr::freq(temp.d$a3_6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Somali")
6. Somali
n % val%
Somali 1 0.1 100
NA 1574 99.9 NA
Total 1575 100.0 100 
a3_7 <- as.factor(d[,"a3_7"])
  levels(a3_7) <- list(Guyanese="1")
  new.d <- data.frame(new.d, a3_7)
  new.d <- apply_labels(new.d, a3_7 = "Guyanese")
  temp.d <- data.frame (new.d, a3_7)
  result<-questionr::freq(temp.d$a3_7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Guyanese")
7. Guyanese
n % val%
Guyanese 3 0.2 100
NA 1572 99.8 NA
Total 1575 100.0 100 
a3_8 <- as.factor(d[,"a3_8"])
  levels(a3_8) <- list(Creole="1")
  new.d <- data.frame(new.d, a3_8)
  new.d <- apply_labels(new.d, a3_8 = "Creole")
  temp.d <- data.frame (new.d, a3_8)
  result<-questionr::freq(temp.d$a3_8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Creole")
8. Creole
n % val%
Creole 3 0.2 100
NA 1572 99.8 NA
Total 1575 100.0 100 
a3_9 <- as.factor(d[,"a3_9"])
  levels(a3_9) <- list(West_Indian="1")
  new.d <- data.frame(new.d, a3_9)
  new.d <- apply_labels(new.d, a3_9 = "West_Indian")
  temp.d <- data.frame (new.d, a3_9)
  result<-questionr::freq(temp.d$a3_9, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. West_Indian")
9. West_Indian
n % val%
West_Indian 29 1.8 100
NA 1546 98.2 NA
Total 1575 100.0 100 
a3_10 <- as.factor(d[,"a3_10"])
  levels(a3_10) <- list(Caribbean="1")
  new.d <- data.frame(new.d, a3_10)
  new.d <- apply_labels(new.d, a3_10 = "Caribbean")
  temp.d <- data.frame (new.d, a3_10)
  result<-questionr::freq(temp.d$a3_10, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Caribbean")
10. Caribbean
n % val%
Caribbean 32 2 100
NA 1543 98 NA
Total 1575 100 100 
a3_11 <- as.factor(d[,"a3_11"])
  levels(a3_11) <- list(White="1")
  new.d <- data.frame(new.d, a3_11)
  new.d <- apply_labels(new.d, a3_11 = "White")
  temp.d <- data.frame (new.d, a3_11)
  result<-questionr::freq(temp.d$a3_11, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "11. White")
11. White
n % val%
White 6 0.4 100
NA 1569 99.6 NA
Total 1575 100.0 100 
a3_12 <- as.factor(d[,"a3_12"])
  levels(a3_12) <- list(Asian="1")
  new.d <- data.frame(new.d, a3_12)
  new.d <- apply_labels(new.d, a3_12 = "Asian")
  temp.d <- data.frame (new.d, a3_12)
  result<-questionr::freq(temp.d$a3_12, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "12. Asian")
12. Asian
n % val%
Asian 1 0.1 100
NA 1574 99.9 NA
Total 1575 100.0 100 
a3_13 <- as.factor(d[,"a3_13"])
  levels(a3_13) <- list(Native_Indian="1")
  new.d <- data.frame(new.d, a3_13)
  new.d <- apply_labels(new.d, a3_13 = "Native_Indian")
  temp.d <- data.frame (new.d, a3_13)
  result<-questionr::freq(temp.d$a3_13, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "13. Native_Indian")
13. Native_Indian
n % val%
Native_Indian 23 1.5 100
NA 1552 98.5 NA
Total 1575 100.0 100 
a3_14 <- as.factor(d[,"a3_14"])
  levels(a3_14) <- list(Middle_Eastern_North_African="1")
  new.d <- data.frame(new.d, a3_14)
  new.d <- apply_labels(new.d, a3_14 = "Middle_Eastern_North_African")
  temp.d <- data.frame (new.d, a3_14)
  result<-questionr::freq(temp.d$a3_14, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "14. Middle_Eastern_North_African")
14. Middle_Eastern_North_African
n % val%
Middle_Eastern_North_African 2 0.1 100
NA 1573 99.9 NA
Total 1575 100.0 100 
a3_15 <- as.factor(d[,"a3_15"])
  levels(a3_15) <- list(Native_Hawaiian_Pacific_Islander="1")
  new.d <- data.frame(new.d, a3_15)
  new.d <- apply_labels(new.d, a3_15 = "Native_Hawaiian_Pacific_Islander")
  temp.d <- data.frame (new.d, a3_15)
  result<-questionr::freq(temp.d$a3_15, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "15. Native_Hawaiian_Pacific_Islander")
15. Native_Hawaiian_Pacific_Islander
n % val%
Native_Hawaiian_Pacific_Islander 0 0 NaN
NA 1575 100 NA
Total 1575 100 100 
a3_16 <- as.factor(d[,"a3_16"])
  levels(a3_16) <- list(Hispanic="1")
  new.d <- data.frame(new.d, a3_16)
  new.d <- apply_labels(new.d, a3_16 = "Hispanic")
  temp.d <- data.frame (new.d, a3_16)
  result<-questionr::freq(temp.d$a3_16, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "16. Hispanic")
16. Hispanic
n % val%
Hispanic 2 0.1 100
NA 1573 99.9 NA
Total 1575 100.0 100 
a3_17 <- as.factor(d[,"a3_17"])
  levels(a3_17) <- list(Latino="1")
  new.d <- data.frame(new.d, a3_17)
  new.d <- apply_labels(new.d, a3_17 = "Latino")
  temp.d <- data.frame (new.d, a3_17)
  result<-questionr::freq(temp.d$a3_17, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "17. Latino")
17. Latino
n % val%
Latino 0 0 NaN
NA 1575 100 NA
Total 1575 100 100 
a3_18 <- as.factor(d[,"a3_18"])
  levels(a3_18) <- list(Spanish="1")
  new.d <- data.frame(new.d, a3_18)
  new.d <- apply_labels(new.d, a3_18 = "Spanish")
  temp.d <- data.frame (new.d, a3_18)
  result<-questionr::freq(temp.d$a3_18, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "18. Spanish")
18. Spanish
n % val%
Spanish 0 0 NaN
NA 1575 100 NA
Total 1575 100 100 
a3_19 <- as.factor(d[,"a3_19"])
  levels(a3_19) <- list(Mexican="1")
  new.d <- data.frame(new.d, a3_19)
  new.d <- apply_labels(new.d, a3_19 = "Mexican")
  temp.d <- data.frame (new.d, a3_19)
  result<-questionr::freq(temp.d$a3_19, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "19. Mexican")
19. Mexican
n % val%
Mexican 0 0 NaN
NA 1575 100 NA
Total 1575 100 100 
a3_20 <- as.factor(d[,"a3_20"])
  levels(a3_20) <- list(Salvadoran="1")
  new.d <- data.frame(new.d, a3_20)
  new.d <- apply_labels(new.d, a3_20 = "Salvadoran")
  temp.d <- data.frame (new.d, a3_20)
  result<-questionr::freq(temp.d$a3_20, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "20. Salvadoran")
20. Salvadoran
n % val%
Salvadoran 0 0 NaN
NA 1575 100 NA
Total 1575 100 100 
a3_21 <- as.factor(d[,"a3_21"])
  levels(a3_21) <- list(Puerto_Rican="1")
  new.d <- data.frame(new.d, a3_21)
  new.d <- apply_labels(new.d, a3_21 = "Puerto_Rican")
  temp.d <- data.frame (new.d, a3_21)
  result<-questionr::freq(temp.d$a3_21, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "21. Puerto_Rican")
21. Puerto_Rican
n % val%
Puerto_Rican 1 0.1 100
NA 1574 99.9 NA
Total 1575 100.0 100 
a3_22 <- as.factor(d[,"a3_22"])
  levels(a3_22) <- list(Dominican="1")
  new.d <- data.frame(new.d, a3_22)
  new.d <- apply_labels(new.d, a3_22 = "Dominican")
  temp.d <- data.frame (new.d, a3_22)
  result<-questionr::freq(temp.d$a3_22, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "22. Dominican")
22. Dominican
n % val%
Dominican 0 0 NaN
NA 1575 100 NA
Total 1575 100 100 
a3_23 <- as.factor(d[,"a3_23"])
  levels(a3_23) <- list(Columbian="1")
  new.d <- data.frame(new.d, a3_23)
  new.d <- apply_labels(new.d, a3_23 = "Columbian")
  temp.d <- data.frame (new.d, a3_23)
  result<-questionr::freq(temp.d$a3_23, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "23. Columbian")
23. Columbian
n % val%
Columbian 0 0 NaN
NA 1575 100 NA
Total 1575 100 100 
a3_24 <- as.factor(d[,"a3_24"])
  levels(a3_23) <- list(Other="1")
  new.d <- data.frame(new.d, a3_24)
  new.d <- apply_labels(new.d, a3_24 = "Other")
  temp.d <- data.frame (new.d, a3_24)
  result<-questionr::freq(temp.d$a3_24, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "24. Other")
24. Other
n % val%
1 20 1.3 100
NA 1555 98.7 NA
Total 1575 100.0 100

A3 Other: Black or African American group

a3other <- d[,"a3other"]
  new.d <- data.frame(new.d, a3other)
  new.d <- apply_labels(new.d, a3other = "A3Other")
  temp.d <- data.frame (new.d, a3other)
result<-questionr::freq(temp.d$a3other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A3Other")
A3Other
n % val%
American Black 1 0.1 2.9
Bahamas 1 0.1 2.9
Barbadion My birth place 1 0.1 2.9
Black 1 0.1 2.9
Blackfoot Indian 1 0.1 2.9
Born in St. Thomas US Virgin Island 1 0.1 2.9
Cameroonian 1 0.1 2.9
Cameroonian. 1 0.1 2.9
Congolese 1 0.1 2.9
English 1 0.1 2.9
Father Panamanian 1 0.1 2.9
From West Africa, Ivory Coast (French Speaking) 1 0.1 2.9
Gambian 1 0.1 2.9
Ghana 1 0.1 2.9
Ghana. 2 0.1 5.9
Ghanaian 3 0.2 8.8
Ghanian 1 0.1 2.9
Greek 1 0.1 2.9
Guinea-CKY 1 0.1 2.9
Human race 2 0.1 5.9
If you need to call me Larry D. Joyner 912-344-5895. 1 0.1 2.9
Jewish 1 0.1 2.9
Liberian African 1 0.1 2.9
Panama is also a country! 1 0.1 2.9
Sierra Leonean 2 0.1 5.9
Tanzanian 1 0.1 2.9
Togo 1 0.1 2.9
Trinidadian 1 0.1 2.9
West African 1 0.1 2.9
NA 1541 97.8 NA
Total 1575 100.0 100.0

A4: Month and year of birth

A4. What is your month and year of birth?

# a4month
a4month <- as.factor(d[,"a4month"])
  new.d <- data.frame(new.d, a4month)
  new.d <- apply_labels(new.d, a4month = "Month of birth")
  temp.d <- data.frame (new.d, a4month) 
  
  result<-questionr::freq(temp.d$a4month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Month of birth")
A4: Month of birth
n % val%
1 141 9.0 9.0
10 128 8.1 8.2
11 113 7.2 7.2
12 152 9.7 9.7
2 116 7.4 7.4
22 1 0.1 0.1
26 1 0.1 0.1
3 140 8.9 8.9
33 1 0.1 0.1
35 1 0.1 0.1
4 119 7.6 7.6
48 1 0.1 0.1
5 132 8.4 8.4
57 1 0.1 0.1
6 119 7.6 7.6
61 1 0.1 0.1
7 124 7.9 7.9
71 1 0.1 0.1
8 162 10.3 10.3
9 113 7.2 7.2
96 1 0.1 0.1
NA 7 0.4 NA
Total 1575 100.0 100.0 
#a4year
a4year <- as.factor(d[,"a4year"])
  new.d <- data.frame(new.d, a4year)
  new.d <- apply_labels(new.d, a4year = "Year of birth")
  temp.d <- data.frame (new.d, a4year) 

  result<-questionr::freq(temp.d$a4year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Year of birth")
A4: Year of birth
n % val%
1062 1 0.1 0.1
1340 1 0.1 0.1
1940 6 0.4 0.4
1941 26 1.7 1.7
1942 32 2.0 2.0
1943 43 2.7 2.7
1944 62 3.9 3.9
1945 52 3.3 3.3
1946 82 5.2 5.2
1947 58 3.7 3.7
1948 106 6.7 6.8
1949 93 5.9 5.9
1950 103 6.5 6.6
1951 92 5.8 5.9
1952 88 5.6 5.6
1953 73 4.6 4.6
1954 72 4.6 4.6
1955 79 5.0 5.0
1956 77 4.9 4.9
1957 75 4.8 4.8
1958 53 3.4 3.4
1959 45 2.9 2.9
1960 56 3.6 3.6
1961 37 2.3 2.4
1962 36 2.3 2.3
1963 28 1.8 1.8
1964 28 1.8 1.8
1965 15 1.0 1.0
1966 12 0.8 0.8
1967 6 0.4 0.4
1968 10 0.6 0.6
1969 5 0.3 0.3
1970 5 0.3 0.3
1971 3 0.2 0.2
1972 3 0.2 0.2
1973 3 0.2 0.2
1974 1 0.1 0.1
1975 1 0.1 0.1
2015 1 0.1 0.1
2018 1 0.1 0.1
NA 5 0.3 NA
Total 1575 100.0 100.0

A5: Where were you born

  • A5. Where were you born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a5 <- as.factor(d[,"a5"])
# Make "*" to NA
a5[which(a5=="*")]<-"NA"
levels(a5) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a5 <- ordered(a5, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a5)
  new.d <- apply_labels(new.d, a5 = "Born place")
  temp.d <- data.frame (new.d, a5) 
  
  result<-questionr::freq(temp.d$a5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5: Where were you born?")
A5: Where were you born?
n % val%
US 1448 91.9 92.6
Africa 46 2.9 2.9
Cuba_Caribbean 45 2.9 2.9
Other 24 1.5 1.5
NA 12 0.8 NA
Total 1575 100.0 100.0

A5 Other: Where were you born

a5other <- d[,"a5other"]
  new.d <- data.frame(new.d, a5other)
  new.d <- apply_labels(new.d, a5other = "a5other")
  temp.d <- data.frame (new.d, a5other)
result<-questionr::freq(temp.d$a5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5Other")
A5Other
n % val%
Barbados 1 0.1 2.2
Beaumont Texas 1 0.1 2.2
Belize Central America 1 0.1 2.2
Central America (Panama) 1 0.1 2.2
Central America Belize 1 0.1 2.2
Chicago IL 1 0.1 2.2
Ethiopia 1 0.1 2.2
Georgia Monroe Walton County 1 0.1 2.2
Germany 1 0.1 2.2
Ghana 1 0.1 2.2
Guinea-CKY 1 0.1 2.2
Guyana 2 0.1 4.4
Haiti 5 0.3 11.1
Heidleburg Germany 1 0.1 2.2
Jamaica 8 0.5 17.8
Jamaica W.I. 1 0.1 2.2
Jamaica, W.I.. 1 0.1 2.2
Jamaica. 1 0.1 2.2
Japan 1 0.1 2.2
Kingston, Jamaica 1 0.1 2.2
Kingston, Jamaica. 1 0.1 2.2
Liberia 1 0.1 2.2
Macon County 1 0.1 2.2
Nassau Bahamas 2 0.1 4.4
Nigeria 1 0.1 2.2
Panama City Panama 1 0.1 2.2
Panama Rep Panama 1 0.1 2.2
Panama. 1 0.1 2.2
Southampton, Bermuda 1 0.1 2.2
Trinidad 1 0.1 2.2
Trinidad and Tobago 1 0.1 2.2
Upson County, GA 1 0.1 2.2
NA 1530 97.1 NA
Total 1575 100.0 100.0

A6: Biological father born

  • A6. Where was your biological father born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a6 <- as.factor(d[,"a6"])
# Make "*" to NA
a6[which(a6=="*")]<-"NA"
levels(a6) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a6 <- ordered(a6, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a6)
  new.d <- apply_labels(new.d, a6 = "Born place")
  temp.d <- data.frame (new.d, a6) 
  
  result<-questionr::freq(temp.d$a6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a6: Where were you born?")
a6: Where were you born?
n % val%
US 1436 91.2 92.2
Africa 46 2.9 3.0
Cuba_Caribbean 50 3.2 3.2
Other 25 1.6 1.6
NA 18 1.1 NA
Total 1575 100.0 100.0

A6 Other: Biological father born

a6other <- d[,"a6other"]
  new.d <- data.frame(new.d, a6other)
  new.d <- apply_labels(new.d, a6other = "a6other")
  temp.d <- data.frame (new.d, a6other)
result<-questionr::freq(temp.d$a6other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A6Other")
A6Other
n % val%
Barbados 1 0.1 2.4
Belize Central America 1 0.1 2.4
Biological father unknown 1 0.1 2.4
Central America (Panama) 1 0.1 2.4
Dead 1 0.1 2.4
Don’t know 1 0.1 2.4
Ethiopia 1 0.1 2.4
Georgia Monroe Walton County 1 0.1 2.4
Ghana 1 0.1 2.4
Guyana 2 0.1 4.8
Haiti 5 0.3 11.9
I don’t know 1 0.1 2.4
I was adopted, no info 1 0.1 2.4
Jamaica 7 0.4 16.7
Jamaica W.I. 1 0.1 2.4
Jamaica. 1 0.1 2.4
Jamaica. W.I.. 1 0.1 2.4
Kingston, Jamaica. 1 0.1 2.4
Macon County 1 0.1 2.4
Mississippi 1 0.1 2.4
Nassau Bahamas 2 0.1 4.8
Nigeria 1 0.1 2.4
Panama City Panama 1 0.1 2.4
Panama Rep Panama 1 0.1 2.4
Panama-Central America 1 0.1 2.4
Panama. 1 0.1 2.4
Possible Puerto Rico 1 0.1 2.4
Trinidad 1 0.1 2.4
Trinidad and Tobago 1 0.1 2.4
Yatesville GA 1 0.1 2.4
NA 1533 97.3 NA
Total 1575 100.0 100.0

A7: Biological mother born

  • A7. Where was your biological mother born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a7 <- as.factor(d[,"a7"])
# Make "*" to NA
a7[which(a7=="*")]<-"NA"
levels(a7) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a7 <- ordered(a7, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a7)
  new.d <- apply_labels(new.d, a7 = "Born place")
  temp.d <- data.frame (new.d, a7) 
  
  result<-questionr::freq(temp.d$a7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a7: Where were you born?")
a7: Where were you born?
n % val%
US 1447 91.9 92.5
Africa 45 2.9 2.9
Cuba_Caribbean 49 3.1 3.1
Other 24 1.5 1.5
NA 10 0.6 NA
Total 1575 100.0 100.0

A7 Other: Biological father born

a7other <- d[,"a7other"]
  new.d <- data.frame(new.d, a7other)
  new.d <- apply_labels(new.d, a7other = "a7other")
  temp.d <- data.frame (new.d, a7other)
result<-questionr::freq(temp.d$a7other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A7Other")
A7Other
n % val%
Barbados 1 0.1 2.5
Belize Central America 1 0.1 2.5
Central America (Colon, Panama) 1 0.1 2.5
Ethiopia 1 0.1 2.5
Georgia Good Hope Walton County 1 0.1 2.5
Germany. 1 0.1 2.5
Ghana 1 0.1 2.5
Guyana 2 0.1 5.0
Haiti 5 0.3 12.5
Jamaica 7 0.4 17.5
Jamaica BWI Kingston 1 0.1 2.5
Jamaica W.I. 1 0.1 2.5
Jamaica, W.I.. 1 0.1 2.5
Jamaica. 1 0.1 2.5
Japan 1 0.1 2.5
Kingston, Jamaica. 1 0.1 2.5
Lamar County, GA 1 0.1 2.5
Macon County 1 0.1 2.5
Mississippi 1 0.1 2.5
Nassau Bahamas 1 0.1 2.5
Nigeria 1 0.1 2.5
No biological info, I was adopted. 1 0.1 2.5
Panama 1 0.1 2.5
Panama City Panama 1 0.1 2.5
Panama Rep Panama 1 0.1 2.5
Panama. 1 0.1 2.5
Puerto Rican 1 0.1 2.5
Trinidad 1 0.1 2.5
Trinidad and Tobago 1 0.1 2.5
NA 1535 97.5 NA
Total 1575 100.0 100.0

A8: Years lived in the US

  • A8. How many years have you lived in the United States?
    • 1=15 years or less
    • 2=16-25 years
    • 3=My whole life or more than 25 years
a8 <- as.factor(d[,"a8"])
# Make "*" to NA
a8[which(a8=="*")]<-"NA"
levels(a8) <- list(less_or_15="1",
                   years_16_25="2",
                   more_than_25_or_whole_life= "3")
  a8 <- ordered(a8, c("less_or_15","years_16_25","more_than_25_or_whole_life"))
  
  new.d <- data.frame(new.d, a8)
  new.d <- apply_labels(new.d, a8 = "Years lived in the US")
  temp.d <- data.frame (new.d, a8) 
  
  result<-questionr::freq(temp.d$a8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A8")
A8
n % val%
less_or_15 10 0.6 0.7
years_16_25 24 1.5 1.6
more_than_25_or_whole_life 1490 94.6 97.8
NA 51 3.2 NA
Total 1575 100.0 100.0

B1A: Father

  • B1Aa: Father: Has this person had prostate cancer?
  • B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
  • B1Ac: Father: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Aa: Father: Has this person had prostate cancer?
  b1aa <- as.factor(d[,"b1aa"])
# Make "*" to NA
b1aa[which(b1aa=="*")]<-"NA"
  levels(b1aa) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1aa <- ordered(b1aa, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1aa)
  new.d <- apply_labels(new.d, b1aa = "Father")
  temp.d <- data.frame (new.d, b1aa)  
  
  result<-questionr::freq(temp.d$b1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Aa: Father: Has this person had prostate cancer?")
B1Aa: Father: Has this person had prostate cancer?
n % val%
No 871 55.3 58.9
Yes 258 16.4 17.4
Dont_know 351 22.3 23.7
NA 95 6.0 NA
Total 1575 100.0 100.0 
#B1Ab: Father: Was he (or any) diagnosed BEFORE age 55? 
  b1ab <- as.factor(d[,"b1ab"])
# Make "*" to NA
b1ab[which(b1ab=="*")]<-"NA"
  levels(b1ab) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ab <- ordered(b1ab, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ab)
  new.d <- apply_labels(new.d, b1ab = "Father")
  temp.d <- data.frame (new.d, b1ab)  
  
  result<-questionr::freq(temp.d$b1ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?")
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 325 20.6 56.9
Yes 33 2.1 5.8
Dont_know 213 13.5 37.3
NA 1004 63.7 NA
Total 1575 100.0 100.0 
#B1Ac: Father: Did he (or any) die of prostate cancer?
  b1ac <- as.factor(d[,"b1ac"])
  # Make "*" to NA
b1ac[which(b1ac=="*")]<-"NA"
  levels(b1ac) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ac <- ordered(b1ac, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ac)
  new.d <- apply_labels(new.d, b1ac = "Father")
  temp.d <- data.frame (new.d, b1ac)  
  
  result<-questionr::freq(temp.d$b1ac,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ac: Father: Did he (or any) die of prostate cancer?")
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 362 23.0 61.8
Yes 97 6.2 16.6
Dont_know 127 8.1 21.7
NA 989 62.8 NA
Total 1575 100.0 100.0

B1B: Any Brother

  • B1BNo: Any Brother
    • 1=I had no brothers
    • if not marked
  • B1Ba: Any Brother: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ba2: Any Brother: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Bc: Any Brother: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1BNo: Any Brother
  b1bno <- as.factor(d[,"b1bno"])
  levels(b1bno) <- list(No_brothers="1")

  new.d <- data.frame(new.d, b1bno)
  new.d <- apply_labels(new.d, b1bno = "Any Brother")
  temp.d <- data.frame (new.d, b1bno)  
  
  result<-questionr::freq(temp.d$b1bno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1BNo: Any Brother")
B1BNo: Any Brother
n % val%
No_brothers 145 9.2 100
NA 1430 90.8 NA
Total 1575 100.0 100 
#B1Ba: Any Brother: Has this person had prostate cancer? 
  b1ba <- as.factor(d[,"b1ba"])
# Make "*" to NA
b1ba[which(b1ba=="*")]<-"NA"
  levels(b1ba) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ba <- ordered(b1ba, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ba)
  new.d <- apply_labels(new.d, b1ba = "Any Brother: have p cancer")
  temp.d <- data.frame (new.d, b1ba)  
  
  result<-questionr::freq(temp.d$b1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba: Any Brother: Has this person had prostate cancer?")
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 918 58.3 66.4
Yes 315 20.0 22.8
Dont_know 150 9.5 10.8
NA 192 12.2 NA
Total 1575 100.0 100.0 
#B1Ba2: Any Brother: If Yes, number with prostate cancer
  b1ba2 <- as.factor(d[,"b1ba2"])
# Make "*" to NA
b1ba2[which(b1ba2=="*")]<-"NA"
  levels(b1ba2) <- list(One="1",
                     Two_or_more="2")
  b1ba2 <- ordered(b1ba2, c("One","Two_or_more"))
  
  new.d <- data.frame(new.d, b1ba2)
  new.d <- apply_labels(new.d, b1ba2 = "Number of brother")
  temp.d <- data.frame (new.d, b1ba2)  
  
  result<-questionr::freq(temp.d$b1ba2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba2: Any Brother: If Yes, number with prostate cancer")
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 136 8.6 62.1
Two_or_more 83 5.3 37.9
NA 1356 86.1 NA
Total 1575 100.0 100.0 
#B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
  b1bb <- as.factor(d[,"b1bb"])
# Make "*" to NA
b1bb[which(b1bb=="*")]<-"NA"
  levels(b1bb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1bb <- ordered(b1bb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1bb)
  new.d <- apply_labels(new.d, b1bb = "Any Brother: before 55")
  temp.d <- data.frame (new.d, b1bb)  
  
  result<-questionr::freq(temp.d$b1bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?")
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 351 22.3 65.4
Yes 65 4.1 12.1
Dont_know 121 7.7 22.5
NA 1038 65.9 NA
Total 1575 100.0 100.0 
#B1Bc: Any Brother: Did he (or any) die of prostate cancer?
  b1bc <- as.factor(d[,"b1bc"])
  # Make "*" to NA
b1bc[which(b1bc=="*")]<-"NA"
  levels(b1bc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1bc <- ordered(b1bc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1bc)
  new.d <- apply_labels(new.d, b1bc = "Any Brother: die")
  temp.d <- data.frame (new.d, b1bc)  
  
  result<-questionr::freq(temp.d$b1bc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bc: Any Brother: Did he (or any) die of prostate cancer?")
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 426 27.0 80.4
Yes 41 2.6 7.7
Dont_know 63 4.0 11.9
NA 1045 66.3 NA
Total 1575 100.0 100.0

B1C: Any Son

  • B1CNo: Any Son
    • 1=I had no sons
    • if not marked
  • B1Ca: Any Son: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ca2: Any Son: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Cc: Any Son: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1BNo
  b1cno <- as.factor(d[,"b1cno"])
  levels(b1cno) <- list(No_brothers="1")

  new.d <- data.frame(new.d, b1cno)
  new.d <- apply_labels(new.d, b1cno = "Any Son")
  temp.d <- data.frame (new.d, b1cno)  
  
  result<-questionr::freq(temp.d$b1cno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1CNo: Any Son")
B1CNo: Any Son
n % val%
No_brothers 276 17.5 100
NA 1299 82.5 NA
Total 1575 100.0 100 
#B1Ca
  b1ca <- as.factor(d[,"b1ca"])
  # Make "*" to NA
b1ca[which(b1ca=="*")]<-"NA"
  levels(b1ca) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ca <- ordered(b1ca, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ca)
  new.d <- apply_labels(new.d, b1ca = "Any Son: have p cancer")
  temp.d <- data.frame (new.d, b1ca)  
  
  result<-questionr::freq(temp.d$b1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca: Any Son: Has this person had prostate cancer?")
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 1142 72.5 92.5
Yes 37 2.3 3.0
Dont_know 55 3.5 4.5
NA 341 21.7 NA
Total 1575 100.0 100.0 
#B1Ca2
  b1ca2 <- as.factor(d[,"b1ca2"])
  # Make "*" to NA
b1ca2[which(b1ca2=="*")]<-"NA"
  levels(b1ca2) <- list(One="1",
                     Two_or_more="2")
  b1ca2 <- ordered(b1ca2, c("One","Two_or_more"))
  
  new.d <- data.frame(new.d, b1ca2)
  new.d <- apply_labels(new.d, b1ca2 = "Number of sons")
  temp.d <- data.frame (new.d, b1ca2)  
  
  result<-questionr::freq(temp.d$b1ca2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca2: Any Son: If Yes, number with prostate cancer")
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 21 1.3 58.3
Two_or_more 15 1.0 41.7
NA 1539 97.7 NA
Total 1575 100.0 100.0 
#B1Cb
  b1cb <- as.factor(d[,"b1cb"])
  # Make "*" to NA
b1cb[which(b1cb=="*")]<-"NA"
  levels(b1cb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1cb <- ordered(b1cb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1cb)
  new.d <- apply_labels(new.d, b1cb = "Any Son: before 55")
  temp.d <- data.frame (new.d, b1cb)  
  
  result<-questionr::freq(temp.d$b1cb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?")
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 287 18.2 85.4
Yes 6 0.4 1.8
Dont_know 43 2.7 12.8
NA 1239 78.7 NA
Total 1575 100.0 100.0 
#B1Cc
  b1cc <- as.factor(d[,"b1cc"])
  # Make "*" to NA
b1cc[which(b1cc=="*")]<-"NA"
  levels(b1cc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1cc <- ordered(b1cc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1cc)
  new.d <- apply_labels(new.d, b1cc = "Any Son: die")
  temp.d <- data.frame (new.d, b1cc)  
  
  result<-questionr::freq(temp.d$b1cc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cc: Any Son: Did he (or any) die of prostate cancer?")
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 300 19.0 89
Yes 0 0.0 0
Dont_know 37 2.3 11
NA 1238 78.6 NA
Total 1575 100.0 100

B1D: Maternal Grandfather

  • B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  • B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  • b1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  b1da <- as.factor(d[,"b1da"])
# Make "*" to NA
b1da[which(b1da=="*")]<-"NA"
  levels(b1da) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1da <- ordered(b1da, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1da)
  new.d <- apply_labels(new.d, b1da = "Father")
  temp.d <- data.frame (new.d, b1da)  
  
  result<-questionr::freq(temp.d$b1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?")
B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 638 40.5 44.5
Yes 32 2.0 2.2
Dont_know 765 48.6 53.3
NA 140 8.9 NA
Total 1575 100.0 100.0 
# B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  b1db <- as.factor(d[,"b1db"])
  # Make "*" to NA
b1db[which(b1db=="*")]<-"NA"
  levels(b1db) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1db <- ordered(b1db, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1db)
  new.d <- apply_labels(new.d, b1db = "Father")
  temp.d <- data.frame (new.d, b1db)  
  
  result<-questionr::freq(temp.d$b1db,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 151 9.6 37.2
Yes 1 0.1 0.2
Dont_know 254 16.1 62.6
NA 1169 74.2 NA
Total 1575 100.0 100.0 
# B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?
  b1dc <- as.factor(d[,"b1dc"])
  # Make "*" to NA
b1dc[which(b1dc=="*")]<-"NA"
  levels(b1dc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1dc <- ordered(b1dc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1dc)
  new.d <- apply_labels(new.d, b1dc = "Father")
  temp.d <- data.frame (new.d, b1dc)  
  
  result<-questionr::freq(temp.d$b1dc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?")
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 163 10.3 38.6
Yes 15 1.0 3.6
Dont_know 244 15.5 57.8
NA 1153 73.2 NA
Total 1575 100.0 100.0

B1E: Paternal Grandfather

  • B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
  • B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  • B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer? 
  b1ea <- as.factor(d[,"b1ea"])
# Make "*" to NA
b1ea[which(b1ea=="*")]<-"NA"
  levels(b1ea) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ea <- ordered(b1ea, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ea)
  new.d <- apply_labels(new.d, b1ea = "Father")
  temp.d <- data.frame (new.d, b1ea)  
  
  result<-questionr::freq(temp.d$b1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?")
B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 579 36.8 40.8
Yes 24 1.5 1.7
Dont_know 815 51.7 57.5
NA 157 10.0 NA
Total 1575 100.0 100.0 
# B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  b1eb <- as.factor(d[,"b1eb"])
  # Make "*" to NA
b1eb[which(b1eb=="*")]<-"NA"
  levels(b1eb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1eb <- ordered(b1eb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1eb)
  new.d <- apply_labels(new.d, b1eb = "Father")
  temp.d <- data.frame (new.d, b1eb)  
  
  result<-questionr::freq(temp.d$b1eb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 122 7.7 31.3
Yes 4 0.3 1.0
Dont_know 264 16.8 67.7
NA 1185 75.2 NA
Total 1575 100.0 100.0 
# B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
  b1ec <- as.factor(d[,"b1ec"])
  # Make "*" to NA
b1ec[which(b1ec=="*")]<-"NA"
  levels(b1ec) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ec <- ordered(b1ec, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ec)
  new.d <- apply_labels(new.d, b1ec = "Father")
  temp.d <- data.frame (new.d, b1ec)  
  
  result<-questionr::freq(temp.d$b1ec,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?")
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 142 9.0 34.5
Yes 15 1.0 3.6
Dont_know 255 16.2 61.9
NA 1163 73.8 NA
Total 1575 100.0 100.0

B2: Family History (Other cancers)

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)?
    • 2=Yes
    • 1=No
b2 <- as.factor(d[,"b2"])
# Make "*" to NA
b2[which(b2=="*")]<-"NA"
levels(b2) <- list(No="1",
                   Yes="2")
  b2 <- ordered(b2, c("Yes","No"))
  
  new.d <- data.frame(new.d, b2)
  new.d <- apply_labels(new.d, b2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, b2) 
  
  result<-questionr::freq(temp.d$b2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B2")
B2
n % val%
Yes 296 18.8 32.9
No 605 38.4 67.1
NA 674 42.8 NA
Total 1575 100.0 100.0

B2A: Mother

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2A_1: 1=Breast
    • B2A_2: 1=Ovarian
    • B2A_3: 1=Colorectal
    • B2A_4: 1=Lung
    • B2A_5: 1=Other Cancer
  b2a_1 <- as.factor(d[,"b2a_1"])
  levels(b2a_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2a_1)
  new.d <- apply_labels(new.d, b2a_1 = "Breast")
  temp.d <- data.frame (new.d, b2a_1)  
  result<-questionr::freq(temp.d$b2a_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 123 7.8 100
NA 1452 92.2 NA
Total 1575 100.0 100 
  b2a_2 <- as.factor(d[,"b2a_2"])
  levels(b2a_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2a_2)
  new.d <- apply_labels(new.d, b2a_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2a_2)  
  result<-questionr::freq(temp.d$b2a_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 66 4.2 100
NA 1509 95.8 NA
Total 1575 100.0 100 
  b2a_3 <- as.factor(d[,"b2a_3"])
  levels(b2a_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2a_3)
  new.d <- apply_labels(new.d, b2a_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2a_3)  
  
  result<-questionr::freq(temp.d$b2a_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 24 1.5 100
NA 1551 98.5 NA
Total 1575 100.0 100 
  b2a_4 <- as.factor(d[,"b2a_4"])
  levels(b2a_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2a_4)
  new.d <- apply_labels(new.d, b2a_4 = "Lung")
  temp.d <- data.frame (new.d, b2a_4)  
  
  result<-questionr::freq(temp.d$b2a_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 44 2.8 100
NA 1531 97.2 NA
Total 1575 100.0 100 
  b2a_5 <- as.factor(d[,"b2a_5"])
  levels(b2a_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2a_5)
  new.d <- apply_labels(new.d, b2a_5 = "Lung")
  temp.d <- data.frame (new.d, b2a_5)  
  
  result<-questionr::freq(temp.d$b2a_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 117 7.4 100
NA 1458 92.6 NA
Total 1575 100.0 100

B2B: Father

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2B_1: 1=Breast
    • B2B_3: 1=Colorectal
    • B2B_4: 1=Lung
    • B2B_5: 1=Other Cancer
  b2b_1 <- as.factor(d[,"b2b_1"])
  levels(b2b_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2b_1)
  new.d <- apply_labels(new.d, b2b_1 = "Breast")
  temp.d <- data.frame (new.d, b2b_1)  
  result<-questionr::freq(temp.d$b2b_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 10 0.6 100
NA 1565 99.4 NA
Total 1575 100.0 100 
  b2b_3 <- as.factor(d[,"b2b_3"])
  levels(b2b_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2b_3)
  new.d <- apply_labels(new.d, b2b_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2b_3)  
  
  result<-questionr::freq(temp.d$b2b_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 25 1.6 100
NA 1550 98.4 NA
Total 1575 100.0 100 
  b2b_4 <- as.factor(d[,"b2b_4"])
  levels(b2b_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2b_4)
  new.d <- apply_labels(new.d, b2b_4 = "Lung")
  temp.d <- data.frame (new.d, b2b_4)  
  
  result<-questionr::freq(temp.d$b2b_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 75 4.8 100
NA 1500 95.2 NA
Total 1575 100.0 100 
  b2b_5 <- as.factor(d[,"b2b_5"])
  levels(b2b_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2b_5)
  new.d <- apply_labels(new.d, b2b_5 = "Lung")
  temp.d <- data.frame (new.d, b2b_5)  
  
  result<-questionr::freq(temp.d$b2b_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 96 6.1 100
NA 1479 93.9 NA
Total 1575 100.0 100

B2C: Any sister

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2C_1: 1=Breast
    • B2C_2: 1=Ovarian
    • B2C_3: 1=Colorectal
    • B2C_4: 1=Lung
    • B2C_5: 1=Other Cancer
  b2c_1 <- as.factor(d[,"b2c_1"])
  levels(b2c_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2c_1)
  new.d <- apply_labels(new.d, b2c_1 = "Breast")
  temp.d <- data.frame (new.d, b2c_1)  
  result<-questionr::freq(temp.d$b2c_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 167 10.6 100
NA 1408 89.4 NA
Total 1575 100.0 100 
  b2c_2 <- as.factor(d[,"b2c_2"])
  levels(b2c_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2c_2)
  new.d <- apply_labels(new.d, b2c_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2c_2)  
  result<-questionr::freq(temp.d$b2c_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 49 3.1 100
NA 1526 96.9 NA
Total 1575 100.0 100 
  b2c_3 <- as.factor(d[,"b2c_3"])
  levels(b2c_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2c_3)
  new.d <- apply_labels(new.d, b2c_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2c_3)  
  
  result<-questionr::freq(temp.d$b2c_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 16 1 100
NA 1559 99 NA
Total 1575 100 100 
  b2c_4 <- as.factor(d[,"b2c_4"])
  levels(b2c_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2c_4)
  new.d <- apply_labels(new.d, b2c_4 = "Lung")
  temp.d <- data.frame (new.d, b2c_4)  
  
  result<-questionr::freq(temp.d$b2c_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 35 2.2 100
NA 1540 97.8 NA
Total 1575 100.0 100 
  b2c_5 <- as.factor(d[,"b2c_5"])
  levels(b2c_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2c_5)
  new.d <- apply_labels(new.d, b2c_5 = "Lung")
  temp.d <- data.frame (new.d, b2c_5)  
  
  result<-questionr::freq(temp.d$b2c_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 86 5.5 100
NA 1489 94.5 NA
Total 1575 100.0 100

B2D: Any brother

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2D_1: 1=Breast
    • B2D_3: 1=Colorectal
    • B2D_4: 1=Lung
    • B2D_5: 1=Other Cancer
  b2d_1 <- as.factor(d[,"b2d_1"])
  levels(b2d_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2d_1)
  new.d <- apply_labels(new.d, b2d_1 = "Breast")
  temp.d <- data.frame (new.d, b2d_1)  
  result<-questionr::freq(temp.d$b2d_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 12 0.8 100
NA 1563 99.2 NA
Total 1575 100.0 100 
  b2d_3 <- as.factor(d[,"b2d_3"])
  levels(b2d_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2d_3)
  new.d <- apply_labels(new.d, b2d_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2d_3)  
  
  result<-questionr::freq(temp.d$b2d_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 17 1.1 100
NA 1558 98.9 NA
Total 1575 100.0 100 
  b2d_4 <- as.factor(d[,"b2d_4"])
  levels(b2d_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2d_4)
  new.d <- apply_labels(new.d, b2d_4 = "Lung")
  temp.d <- data.frame (new.d, b2d_4)  
  
  result<-questionr::freq(temp.d$b2d_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 50 3.2 100
NA 1525 96.8 NA
Total 1575 100.0 100 
  b2d_5 <- as.factor(d[,"b2d_5"])
  levels(b2d_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2d_5)
  new.d <- apply_labels(new.d, b2d_5 = "Lung")
  temp.d <- data.frame (new.d, b2d_5)  
  
  result<-questionr::freq(temp.d$b2d_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 107 6.8 100
NA 1468 93.2 NA
Total 1575 100.0 100

B2E: Any daughter

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2E_1: 1=Breast
    • B2E_2: 1=Ovarian
    • B2E_3: 1=Colorectal
    • B2E_4: 1=Lung
    • B2E_5: 1=Other Cancer
  b2e_1 <- as.factor(d[,"b2e_1"])
  levels(b2e_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2e_1)
  new.d <- apply_labels(new.d, b2e_1 = "Breast")
  temp.d <- data.frame (new.d, b2e_1)  
  result<-questionr::freq(temp.d$b2e_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 12 0.8 100
NA 1563 99.2 NA
Total 1575 100.0 100 
  b2e_2 <- as.factor(d[,"b2e_2"])
  levels(b2e_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2e_2)
  new.d <- apply_labels(new.d, b2e_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2e_2)  
  result<-questionr::freq(temp.d$b2e_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 31 2 100
NA 1544 98 NA
Total 1575 100 100 
  b2e_3 <- as.factor(d[,"b2e_3"])
  levels(b2e_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2e_3)
  new.d <- apply_labels(new.d, b2e_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2e_3)  
  
  result<-questionr::freq(temp.d$b2e_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 2 0.1 100
NA 1573 99.9 NA
Total 1575 100.0 100 
  b2e_4 <- as.factor(d[,"b2e_4"])
  levels(b2e_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2e_4)
  new.d <- apply_labels(new.d, b2e_4 = "Lung")
  temp.d <- data.frame (new.d, b2e_4)  
  
  result<-questionr::freq(temp.d$b2e_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 1 0.1 100
NA 1574 99.9 NA
Total 1575 100.0 100 
  b2e_5 <- as.factor(d[,"b2e_5"])
  levels(b2e_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2e_5)
  new.d <- apply_labels(new.d, b2e_5 = "Lung")
  temp.d <- data.frame (new.d, b2e_5)  
  
  result<-questionr::freq(temp.d$b2e_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 6 0.4 100
NA 1569 99.6 NA
Total 1575 100.0 100

B2F: Any son

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2F_1: 1=Breast
    • B2F_3: 1=Colorectal
    • B2F_4: 1=Lung
    • B2F_5: 1=Other Cancer
  b2f_1 <- as.factor(d[,"b2f_1"])
  levels(b2f_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2f_1)
  new.d <- apply_labels(new.d, b2f_1 = "Breast")
  temp.d <- data.frame (new.d, b2f_1)  
  result<-questionr::freq(temp.d$b2f_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 6 0.4 100
NA 1569 99.6 NA
Total 1575 100.0 100 
  b2f_3 <- as.factor(d[,"b2f_3"])
  levels(b2f_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2f_3)
  new.d <- apply_labels(new.d, b2f_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2f_3)  
  
  result<-questionr::freq(temp.d$b2f_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 4 0.3 100
NA 1571 99.7 NA
Total 1575 100.0 100 
  b2f_4 <- as.factor(d[,"b2f_4"])
  levels(b2f_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2f_4)
  new.d <- apply_labels(new.d, b2f_4 = "Lung")
  temp.d <- data.frame (new.d, b2f_4)  
  
  result<-questionr::freq(temp.d$b2f_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 5 0.3 100
NA 1570 99.7 NA
Total 1575 100.0 100 
  b2f_5 <- as.factor(d[,"b2f_5"])
  levels(b2f_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2f_5)
  new.d <- apply_labels(new.d, b2f_5 = "Lung")
  temp.d <- data.frame (new.d, b2f_5)  
  
  result<-questionr::freq(temp.d$b2f_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 10 0.6 100
NA 1565 99.4 NA
Total 1575 100.0 100

B3: Current health

  • B3. In general, how would you rate your current health?
    • 1=Excellent
    • 2=Very Good
    • 3=Good
    • 4=Fair
    • 5=Poor
  b3 <- as.factor(d[,"b3"])
# Make "*" to NA
b3[which(b3=="*")]<-"NA"
  levels(b3) <- list(Excellent="1",
                     Very_Good="2",
                     Good="3",
                     Fair="4",
                     Poor="5")
  b3 <- ordered(b3, c("Excellent","Very_Good","Good","Fair","Poor"))

  new.d <- data.frame(new.d, b3)
  new.d <- apply_labels(new.d, b3 = "Current Health")
  temp.d <- data.frame (new.d, b3)  
  
  result<-questionr::freq(temp.d$b3, cum = TRUE, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Excellent 83 5.3 5.6 5.3 5.6
Very_Good 363 23.0 24.4 28.3 29.9
Good 680 43.2 45.6 71.5 75.6
Fair 334 21.2 22.4 92.7 98.0
Poor 30 1.9 2.0 94.6 100.0
NA 85 5.4 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

B4: Comorbidities

  • B4. Has the doctor ever told you that you have/had…
    • Heart Attack
    • Heart Failure or CHF
    • Stroke
    • Hypertension
    • Peripheral arterial disease
    • High Cholesterol
    • Asthma, COPD
    • Stomach ulcers
    • Crohn’s Disease
    • Diabetes
    • Kidney Problems
    • Cirrhosis, liver damage
    • Arthritis
    • Dementia
    • Depression
    • AIDS
    • Other Cancer
# Heart Attack
  b4aa <- as.factor(d[,"b4aa"])
# Make "*" to NA
b4aa[which(b4aa=="*")]<-"NA"
  levels(b4aa) <- list(No="1",
                     Yes="2")
  b4aa <- ordered(b4aa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4aa)
  new.d <- apply_labels(new.d, b4aa = "Heart Attack")
  temp.d <- data.frame (new.d, b4aa)  
  
  result<-questionr::freq(temp.d$b4aa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack")
Heart Attack
n % val%
No 1366 86.7 93.3
Yes 98 6.2 6.7
NA 111 7.0 NA
Total 1575 100.0 100.0 
  b4ab <- as.factor(d[,"b4ab"])
  new.d <- data.frame(new.d, b4ab)
  new.d <- apply_labels(new.d, b4ab = "Heart Attack age")
  temp.d <- data.frame (new.d, b4ab)  
  result<-questionr::freq(temp.d$b4ab, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack Age")
Heart Attack Age
n % val%
1 1 0.1 1.1
17 1 0.1 1.1
24 1 0.1 1.1
25 1 0.1 1.1
27 1 0.1 1.1
29 1 0.1 1.1
35 2 0.1 2.2
38 1 0.1 1.1
40 2 0.1 2.2
45 1 0.1 1.1
46 2 0.1 2.2
47 1 0.1 1.1
48 3 0.2 3.4
49 1 0.1 1.1
5 1 0.1 1.1
50 8 0.5 9.0
52 4 0.3 4.5
53 1 0.1 1.1
54 1 0.1 1.1
55 4 0.3 4.5
57 4 0.3 4.5
58 5 0.3 5.6
59 4 0.3 4.5
60 6 0.4 6.7
61 3 0.2 3.4
62 1 0.1 1.1
63 4 0.3 4.5
64 3 0.2 3.4
65 3 0.2 3.4
66 1 0.1 1.1
67 3 0.2 3.4
68 2 0.1 2.2
69 3 0.2 3.4
70 1 0.1 1.1
71 3 0.2 3.4
73 2 0.1 2.2
74 3 0.2 3.4
NA 1486 94.3 NA
Total 1575 100.0 100.0 
# Heart Failure or CHF
  b4ba <- as.factor(d[,"b4ba"])
  # Make "*" to NA
b4ba[which(b4ba=="*")]<-"NA"
  levels(b4ba) <- list(No="1",
                     Yes="2")
  b4ba <- ordered(b4ba, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ba)
  new.d <- apply_labels(new.d, b4ba = "Heart Failure or CHF")
  temp.d <- data.frame (new.d, b4ba)  
  
  result<-questionr::freq(temp.d$b4ba, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF")
Heart Failure or CHF
n % val%
No 1358 86.2 93.8
Yes 90 5.7 6.2
NA 127 8.1 NA
Total 1575 100.0 100.0 
  b4bb <- as.factor(d[,"b4bb"])
  new.d <- data.frame(new.d, b4bb)
  new.d <- apply_labels(new.d, b4bb = "Heart Failure or CHF age")
  temp.d <- data.frame (new.d, b4bb)  
  result<-questionr::freq(temp.d$b4bb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF Age")
Heart Failure or CHF Age
n % val%
20 1 0.1 1.3
30 1 0.1 1.3
35 1 0.1 1.3
40 1 0.1 1.3
43 1 0.1 1.3
48 3 0.2 4.0
49 4 0.3 5.3
5 1 0.1 1.3
50 1 0.1 1.3
51 2 0.1 2.7
52 1 0.1 1.3
53 3 0.2 4.0
54 2 0.1 2.7
55 3 0.2 4.0
56 1 0.1 1.3
57 2 0.1 2.7
58 4 0.3 5.3
59 4 0.3 5.3
60 4 0.3 5.3
61 3 0.2 4.0
62 4 0.3 5.3
63 1 0.1 1.3
65 6 0.4 8.0
66 2 0.1 2.7
67 3 0.2 4.0
68 3 0.2 4.0
69 4 0.3 5.3
70 2 0.1 2.7
71 1 0.1 1.3
72 2 0.1 2.7
73 1 0.1 1.3
74 1 0.1 1.3
75 1 0.1 1.3
82 1 0.1 1.3
NA 1500 95.2 NA
Total 1575 100.0 100.0 
# Stroke  
  b4ca <- as.factor(d[,"b4ca"])
  # Make "*" to NA
b4ca[which(b4ca=="*")]<-"NA"
  levels(b4ca) <- list(No="1",
                     Yes="2")
  b4ca <- ordered(b4ca, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ca)
  new.d <- apply_labels(new.d, b4ca = "Stroke")
  temp.d <- data.frame (new.d, b4ca)  
  
  result<-questionr::freq(temp.d$b4ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke")
Stroke
n % val%
No 1310 83.2 90.2
Yes 142 9.0 9.8
NA 123 7.8 NA
Total 1575 100.0 100.0 
  b4cb <- as.factor(d[,"b4cb"])
  new.d <- data.frame(new.d, b4cb)
  new.d <- apply_labels(new.d, b4cb = "Stroke age")
  temp.d <- data.frame (new.d, b4cb)  
  result<-questionr::freq(temp.d$b4cb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke Age")
Stroke Age
n % val%
16 1 0.1 0.8
19 1 0.1 0.8
30 1 0.1 0.8
35 1 0.1 0.8
38 1 0.1 0.8
40 1 0.1 0.8
43 2 0.1 1.7
45 4 0.3 3.3
46 2 0.1 1.7
47 1 0.1 0.8
48 1 0.1 0.8
5 1 0.1 0.8
50 5 0.3 4.1
51 2 0.1 1.7
52 2 0.1 1.7
53 2 0.1 1.7
54 4 0.3 3.3
55 5 0.3 4.1
56 3 0.2 2.5
57 4 0.3 3.3
58 4 0.3 3.3
59 5 0.3 4.1
60 6 0.4 5.0
61 7 0.4 5.8
62 4 0.3 3.3
63 6 0.4 5.0
64 6 0.4 5.0
65 8 0.5 6.6
66 3 0.2 2.5
67 3 0.2 2.5
68 5 0.3 4.1
69 8 0.5 6.6
70 2 0.1 1.7
71 2 0.1 1.7
73 1 0.1 0.8
74 2 0.1 1.7
75 1 0.1 0.8
76 1 0.1 0.8
78 3 0.2 2.5
NA 1454 92.3 NA
Total 1575 100.0 100.0 
# Hypertension 
  b4da <- as.factor(d[,"b4da"])
# Make "*" to NA
b4da[which(b4da=="*")]<-"NA"
  levels(b4da) <- list(No="1",
                     Yes="2")
  b4da <- ordered(b4da, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4da)
  new.d <- apply_labels(new.d, b4da = "Hypertension")
  temp.d <- data.frame (new.d, b4da)  
  
  result<-questionr::freq(temp.d$b4da, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension")
Hypertension
n % val%
No 336 21.3 22.7
Yes 1142 72.5 77.3
NA 97 6.2 NA
Total 1575 100.0 100.0 
  b4db <- as.factor(d[,"b4db"])
  new.d <- data.frame(new.d, b4db)
  new.d <- apply_labels(new.d, b4db = "Hypertension age")
  temp.d <- data.frame (new.d, b4db)  
  result<-questionr::freq(temp.d$b4db, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension Age")
Hypertension Age
n % val%
1 1 0.1 0.1
12 1 0.1 0.1
13 1 0.1 0.1
14 1 0.1 0.1
15 3 0.2 0.3
16 5 0.3 0.5
17 2 0.1 0.2
18 2 0.1 0.2
19 2 0.1 0.2
2 1 0.1 0.1
20 4 0.3 0.4
21 1 0.1 0.1
22 2 0.1 0.2
23 4 0.3 0.4
24 5 0.3 0.5
25 11 0.7 1.1
26 1 0.1 0.1
27 4 0.3 0.4
28 5 0.3 0.5
29 2 0.1 0.2
30 33 2.1 3.3
31 7 0.4 0.7
32 4 0.3 0.4
33 4 0.3 0.4
34 4 0.3 0.4
35 35 2.2 3.5
36 8 0.5 0.8
37 4 0.3 0.4
38 10 0.6 1.0
39 7 0.4 0.7
4 1 0.1 0.1
40 85 5.4 8.6
41 4 0.3 0.4
42 11 0.7 1.1
43 7 0.4 0.7
44 9 0.6 0.9
45 68 4.3 6.8
46 9 0.6 0.9
47 11 0.7 1.1
48 12 0.8 1.2
49 10 0.6 1.0
5 1 0.1 0.1
50 107 6.8 10.8
51 11 0.7 1.1
52 27 1.7 2.7
53 8 0.5 0.8
54 19 1.2 1.9
55 76 4.8 7.7
56 21 1.3 2.1
57 18 1.1 1.8
58 30 1.9 3.0
59 17 1.1 1.7
60 71 4.5 7.2
61 19 1.2 1.9
62 21 1.3 2.1
63 11 0.7 1.1
64 22 1.4 2.2
65 30 1.9 3.0
66 6 0.4 0.6
67 13 0.8 1.3
68 13 0.8 1.3
69 8 0.5 0.8
70 18 1.1 1.8
71 9 0.6 0.9
72 3 0.2 0.3
73 2 0.1 0.2
75 1 0.1 0.1
77 1 0.1 0.1
8 3 0.2 0.3
89 1 0.1 0.1
92 1 0.1 0.1
93 1 0.1 0.1
94 1 0.1 0.1
96 1 0.1 0.1
99 1 0.1 0.1
NA 582 37.0 NA
Total 1575 100.0 100.0 
# Peripheral arterial disease 
  b4ea <- as.factor(d[,"b4ea"])
# Make "*" to NA
b4ea[which(b4ea=="*")]<-"NA"  
  levels(b4ea) <- list(No="1",
                     Yes="2")
  b4ea <- ordered(b4ea, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ea)
  new.d <- apply_labels(new.d, b4ea = "Peripheral arterial disease")
  temp.d <- data.frame (new.d, b4ea)  
  
  result<-questionr::freq(temp.d$b4ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease")
Peripheral arterial disease
n % val%
No 1322 83.9 93.8
Yes 87 5.5 6.2
NA 166 10.5 NA
Total 1575 100.0 100.0 
  b4eb <- as.factor(d[,"b4eb"])
  new.d <- data.frame(new.d, b4eb)
  new.d <- apply_labels(new.d, b4eb = "Peripheral arterial disease age")
  temp.d <- data.frame (new.d, b4eb)  
  result<-questionr::freq(temp.d$b4eb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease Age")
Peripheral arterial disease Age
n % val%
* 1 0.1 0.9
** 1 0.1 0.9
0 1 0.1 0.9
1 1 0.1 0.9
16 1 0.1 0.9
19 1 0.1 0.9
25 1 0.1 0.9
30 1 0.1 0.9
33 1 0.1 0.9
34 1 0.1 0.9
35 4 0.3 3.5
37 1 0.1 0.9
40 7 0.4 6.1
42 1 0.1 0.9
45 4 0.3 3.5
46 1 0.1 0.9
47 1 0.1 0.9
48 1 0.1 0.9
49 2 0.1 1.8
50 10 0.6 8.8
53 1 0.1 0.9
54 1 0.1 0.9
55 7 0.4 6.1
56 1 0.1 0.9
57 1 0.1 0.9
58 3 0.2 2.6
59 2 0.1 1.8
60 10 0.6 8.8
62 5 0.3 4.4
63 3 0.2 2.6
65 9 0.6 7.9
66 2 0.1 1.8
67 6 0.4 5.3
68 5 0.3 4.4
69 3 0.2 2.6
70 4 0.3 3.5
71 1 0.1 0.9
72 1 0.1 0.9
73 2 0.1 1.8
74 2 0.1 1.8
78 1 0.1 0.9
84 1 0.1 0.9
97 1 0.1 0.9
NA 1461 92.8 NA
Total 1575 100.0 100.0 
# High Cholesterol 
  b4fa <- as.factor(d[,"b4fa"])
  # Make "*" to NA
b4fa[which(b4fa=="*")]<-"NA"
  levels(b4fa) <- list(No="1",
                     Yes="2")
  b4fa <- ordered(b4fa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4fa)
  new.d <- apply_labels(new.d, b4fa = "High Cholesterol")
  temp.d <- data.frame (new.d, b4fa)  
  
  result<-questionr::freq(temp.d$b4fa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol")
High Cholesterol
n % val%
No 642 40.8 44.4
Yes 803 51.0 55.6
NA 130 8.3 NA
Total 1575 100.0 100.0 
  b4fb <- as.factor(d[,"b4fb"])
  new.d <- data.frame(new.d, b4fb)
  new.d <- apply_labels(new.d, b4fb = "High Cholesterol age")
  temp.d <- data.frame (new.d, b4fb)  
  result<-questionr::freq(temp.d$b4fb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol Age")
High Cholesterol Age
n % val%
1 2 0.1 0.3
10 1 0.1 0.1
12 1 0.1 0.1
14 1 0.1 0.1
15 1 0.1 0.1
16 1 0.1 0.1
2 1 0.1 0.1
21 3 0.2 0.4
22 1 0.1 0.1
24 4 0.3 0.6
25 4 0.3 0.6
27 1 0.1 0.1
28 2 0.1 0.3
30 7 0.4 1.0
31 2 0.1 0.3
32 3 0.2 0.4
34 4 0.3 0.6
35 14 0.9 2.1
36 6 0.4 0.9
37 1 0.1 0.1
38 8 0.5 1.2
39 3 0.2 0.4
4 1 0.1 0.1
40 37 2.3 5.5
41 3 0.2 0.4
42 3 0.2 0.4
43 3 0.2 0.4
44 4 0.3 0.6
45 36 2.3 5.4
46 9 0.6 1.3
47 6 0.4 0.9
48 12 0.8 1.8
49 7 0.4 1.0
5 2 0.1 0.3
50 82 5.2 12.2
51 7 0.4 1.0
52 11 0.7 1.6
53 8 0.5 1.2
54 14 0.9 2.1
55 56 3.6 8.4
56 12 0.8 1.8
57 19 1.2 2.8
58 17 1.1 2.5
59 17 1.1 2.5
60 66 4.2 9.9
61 12 0.8 1.8
62 26 1.7 3.9
63 10 0.6 1.5
64 14 0.9 2.1
65 27 1.7 4.0
66 6 0.4 0.9
67 9 0.6 1.3
68 11 0.7 1.6
69 13 0.8 1.9
70 13 0.8 1.9
71 4 0.3 0.6
72 9 0.6 1.3
73 1 0.1 0.1
74 3 0.2 0.4
75 2 0.1 0.3
76 3 0.2 0.4
8 1 0.1 0.1
95 1 0.1 0.1
96 1 0.1 0.1
97 1 0.1 0.1
NA 905 57.5 NA
Total 1575 100.0 100.0 
#  Asthma, COPD
  b4ga <- as.factor(d[,"b4ga"])
  # Make "*" to NA
b4ga[which(b4ga=="*")]<-"NA"
  levels(b4ga) <- list(No="1",
                     Yes="2")
  b4ga <- ordered(b4ga, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ga)
  new.d <- apply_labels(new.d, b4ga = "Asthma, COPD")
  temp.d <- data.frame (new.d, b4ga)  
  
  result<-questionr::freq(temp.d$b4ga, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD")
Asthma, COPD
n % val%
No 1314 83.4 86.4
Yes 206 13.1 13.6
NA 55 3.5 NA
Total 1575 100.0 100.0 
  b4gb <- as.factor(d[,"b4gb"])
  new.d <- data.frame(new.d, b4gb)
  new.d <- apply_labels(new.d, b4gb = "Asthma, COPD age")
  temp.d <- data.frame (new.d, b4gb)  
  result<-questionr::freq(temp.d$b4gb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD Age")
Asthma, COPD Age
n % val%
1 5 0.3 2.7
10 10 0.6 5.4
12 4 0.3 2.2
13 1 0.1 0.5
14 1 0.1 0.5
15 2 0.1 1.1
16 3 0.2 1.6
18 8 0.5 4.3
2 3 0.2 1.6
23 1 0.1 0.5
25 3 0.2 1.6
28 2 0.1 1.1
29 1 0.1 0.5
30 5 0.3 2.7
32 1 0.1 0.5
35 2 0.1 1.1
38 5 0.3 2.7
4 2 0.1 1.1
40 6 0.4 3.3
45 6 0.4 3.3
46 1 0.1 0.5
47 2 0.1 1.1
48 2 0.1 1.1
49 1 0.1 0.5
5 10 0.6 5.4
50 4 0.3 2.2
51 1 0.1 0.5
52 3 0.2 1.6
53 2 0.1 1.1
54 1 0.1 0.5
55 7 0.4 3.8
56 2 0.1 1.1
57 3 0.2 1.6
58 3 0.2 1.6
59 5 0.3 2.7
6 4 0.3 2.2
60 10 0.6 5.4
61 4 0.3 2.2
62 3 0.2 1.6
63 3 0.2 1.6
64 2 0.1 1.1
65 7 0.4 3.8
66 1 0.1 0.5
67 3 0.2 1.6
68 5 0.3 2.7
69 3 0.2 1.6
7 2 0.1 1.1
70 2 0.1 1.1
71 3 0.2 1.6
72 3 0.2 1.6
73 4 0.3 2.2
77 1 0.1 0.5
8 2 0.1 1.1
9 4 0.3 2.2
NA 1391 88.3 NA
Total 1575 100.0 100.0 
# Stomach ulcers
  b4ha <- as.factor(d[,"b4ha"])
  # Make "*" to NA
b4ha[which(b4ha=="*")]<-"NA"
  levels(b4ha) <- list(No="1",
                     Yes="2")
  b4ha <- ordered(b4ha, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ha)
  new.d <- apply_labels(new.d, b4ha = "Stomach ulcers")
  temp.d <- data.frame (new.d, b4ha)  
  
  result<-questionr::freq(temp.d$b4ha, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers")
Stomach ulcers
n % val%
No 1375 87.3 90.6
Yes 142 9.0 9.4
NA 58 3.7 NA
Total 1575 100.0 100.0 
  b4hb <- as.factor(d[,"b4hb"])
  new.d <- data.frame(new.d, b4hb)
  new.d <- apply_labels(new.d, b4hb = "Stomach ulcers age")
  temp.d <- data.frame (new.d, b4hb)  
  result<-questionr::freq(temp.d$b4hb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers Age")
Stomach ulcers Age
n % val%
10 1 0.1 0.8
14 2 0.1 1.6
16 1 0.1 0.8
18 3 0.2 2.4
2 1 0.1 0.8
20 2 0.1 1.6
22 2 0.1 1.6
23 1 0.1 0.8
24 1 0.1 0.8
25 2 0.1 1.6
27 2 0.1 1.6
28 1 0.1 0.8
30 9 0.6 7.3
31 1 0.1 0.8
32 2 0.1 1.6
33 1 0.1 0.8
34 1 0.1 0.8
35 6 0.4 4.9
36 2 0.1 1.6
38 2 0.1 1.6
39 2 0.1 1.6
40 10 0.6 8.1
44 1 0.1 0.8
45 9 0.6 7.3
47 2 0.1 1.6
48 3 0.2 2.4
49 1 0.1 0.8
50 7 0.4 5.7
51 1 0.1 0.8
52 2 0.1 1.6
53 1 0.1 0.8
55 1 0.1 0.8
56 1 0.1 0.8
57 1 0.1 0.8
58 2 0.1 1.6
59 1 0.1 0.8
60 4 0.3 3.3
61 2 0.1 1.6
62 5 0.3 4.1
63 1 0.1 0.8
64 2 0.1 1.6
65 2 0.1 1.6
66 1 0.1 0.8
67 4 0.3 3.3
68 1 0.1 0.8
69 2 0.1 1.6
70 1 0.1 0.8
71 1 0.1 0.8
72 3 0.2 2.4
73 1 0.1 0.8
74 1 0.1 0.8
76 1 0.1 0.8
8 1 0.1 0.8
9 1 0.1 0.8
94 1 0.1 0.8
NA 1452 92.2 NA
Total 1575 100.0 100.0 
# Crohn's Disease
  b4ia <- as.factor(d[,"b4ia"])
  # Make "*" to NA
b4ia[which(b4ia=="*")]<-"NA"
  levels(b4ia) <- list(No="1",
                     Yes="2")
  b4ia <- ordered(b4ia, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ia)
  new.d <- apply_labels(new.d, b4ia = "Crohn's Disease")
  temp.d <- data.frame (new.d, b4ia)  
  
  result<-questionr::freq(temp.d$b4ia, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease")
Crohn’s Disease
n % val%
No 1455 92.4 96.4
Yes 55 3.5 3.6
NA 65 4.1 NA
Total 1575 100.0 100.0 
  b4ib <- as.factor(d[,"b4ib"])
  new.d <- data.frame(new.d, b4ib)
  new.d <- apply_labels(new.d, b4ib = "Crohn's Disease age")
  temp.d <- data.frame (new.d, b4ib)  
  result<-questionr::freq(temp.d$b4ib, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease Age")
Crohn’s Disease Age
n % val%
2 1 0.1 1.9
30 1 0.1 1.9
32 1 0.1 1.9
35 2 0.1 3.8
37 1 0.1 1.9
38 1 0.1 1.9
39 1 0.1 1.9
40 6 0.4 11.5
41 1 0.1 1.9
43 1 0.1 1.9
44 2 0.1 3.8
45 1 0.1 1.9
47 1 0.1 1.9
48 1 0.1 1.9
50 2 0.1 3.8
51 1 0.1 1.9
52 2 0.1 3.8
53 1 0.1 1.9
54 2 0.1 3.8
55 2 0.1 3.8
56 1 0.1 1.9
58 2 0.1 3.8
59 1 0.1 1.9
60 4 0.3 7.7
63 3 0.2 5.8
65 1 0.1 1.9
66 1 0.1 1.9
69 2 0.1 3.8
70 1 0.1 1.9
71 1 0.1 1.9
74 1 0.1 1.9
75 1 0.1 1.9
76 1 0.1 1.9
85 1 0.1 1.9
NA 1523 96.7 NA
Total 1575 100.0 100.0 
# Diabetes
  b4ja <- as.factor(d[,"b4ja"])
  # Make "*" to NA
b4ja[which(b4ja=="*")]<-"NA"
  levels(b4ja) <- list(No="1",
                     Yes="2")
  b4ja <- ordered(b4ja, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ja)
  new.d <- apply_labels(new.d, b4ja = "Diabetes")
  temp.d <- data.frame (new.d, b4ja)  
  
  result<-questionr::freq(temp.d$b4ja, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes")
Diabetes
n % val%
No 1008 64.0 66.2
Yes 515 32.7 33.8
NA 52 3.3 NA
Total 1575 100.0 100.0 
  b4jb <- as.factor(d[,"b4jb"])
  new.d <- data.frame(new.d, b4jb)
  new.d <- apply_labels(new.d, b4jb = "Diabetes age")
  temp.d <- data.frame (new.d, b4jb)  
  result<-questionr::freq(temp.d$b4jb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes Age")
Diabetes Age
n % val%
0 2 0.1 0.4
10 1 0.1 0.2
12 1 0.1 0.2
13 2 0.1 0.4
14 1 0.1 0.2
15 1 0.1 0.2
16 1 0.1 0.2
17 1 0.1 0.2
2 1 0.1 0.2
20 1 0.1 0.2
24 1 0.1 0.2
27 1 0.1 0.2
28 1 0.1 0.2
29 1 0.1 0.2
30 7 0.4 1.6
31 1 0.1 0.2
32 1 0.1 0.2
34 1 0.1 0.2
35 12 0.8 2.7
37 3 0.2 0.7
38 3 0.2 0.7
39 3 0.2 0.7
40 24 1.5 5.4
42 4 0.3 0.9
43 5 0.3 1.1
44 5 0.3 1.1
45 24 1.5 5.4
46 3 0.2 0.7
47 7 0.4 1.6
48 5 0.3 1.1
49 3 0.2 0.7
5 1 0.1 0.2
50 43 2.7 9.6
51 4 0.3 0.9
52 8 0.5 1.8
53 11 0.7 2.5
54 9 0.6 2.0
55 30 1.9 6.7
56 12 0.8 2.7
57 10 0.6 2.2
58 15 1.0 3.4
59 13 0.8 2.9
60 37 2.3 8.3
61 19 1.2 4.3
62 14 0.9 3.1
63 9 0.6 2.0
64 13 0.8 2.9
65 19 1.2 4.3
66 6 0.4 1.3
67 6 0.4 1.3
68 9 0.6 2.0
69 7 0.4 1.6
70 10 0.6 2.2
71 4 0.3 0.9
72 4 0.3 0.9
73 1 0.1 0.2
74 2 0.1 0.4
75 2 0.1 0.4
78 1 0.1 0.2
97 1 0.1 0.2
NA 1128 71.6 NA
Total 1575 100.0 100.0 
# Kidney Problems
  b4ka <- as.factor(d[,"b4ka"])
  # Make "*" to NA
b4ka[which(b4ka=="*")]<-"NA"
  levels(b4ka) <- list(No="1",
                     Yes="2")
  b4ka <- ordered(b4ka, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ka)
  new.d <- apply_labels(new.d, b4ka = "Kidney Problems")
  temp.d <- data.frame (new.d, b4ka)  
  
  result<-questionr::freq(temp.d$b4ka, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems")
Kidney Problems
n % val%
No 1432 90.9 93.8
Yes 94 6.0 6.2
NA 49 3.1 NA
Total 1575 100.0 100.0 
  b4kb <- as.factor(d[,"b4kb"])
  new.d <- data.frame(new.d, b4kb)
  new.d <- apply_labels(new.d, b4kb = "Kidney Problems age")
  temp.d <- data.frame (new.d, b4kb)  
  result<-questionr::freq(temp.d$b4kb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems Age")
Kidney Problems Age
n % val%
15 1 0.1 1.3
16 1 0.1 1.3
17 1 0.1 1.3
24 1 0.1 1.3
3 1 0.1 1.3
35 1 0.1 1.3
40 4 0.3 5.1
42 1 0.1 1.3
44 1 0.1 1.3
45 1 0.1 1.3
46 1 0.1 1.3
48 1 0.1 1.3
50 3 0.2 3.8
51 2 0.1 2.5
52 1 0.1 1.3
53 1 0.1 1.3
54 4 0.3 5.1
55 5 0.3 6.3
56 5 0.3 6.3
58 1 0.1 1.3
59 2 0.1 2.5
61 3 0.2 3.8
62 5 0.3 6.3
63 2 0.1 2.5
64 5 0.3 6.3
65 4 0.3 5.1
66 3 0.2 3.8
67 3 0.2 3.8
68 1 0.1 1.3
69 3 0.2 3.8
7 1 0.1 1.3
70 4 0.3 5.1
71 1 0.1 1.3
73 1 0.1 1.3
75 3 0.2 3.8
76 1 0.1 1.3
NA 1496 95.0 NA
Total 1575 100.0 100.0 
# Cirrhosis, liver damage
  b4la <- as.factor(d[,"b4la"])
  # Make "*" to NA
b4la[which(b4la=="*")]<-"NA"
  levels(b4la) <- list(No="1",
                     Yes="2")
  b4la <- ordered(b4la, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4la)
  new.d <- apply_labels(new.d, b4la = "Cirrhosis, liver damage")
  temp.d <- data.frame (new.d, b4la)  
  
  result<-questionr::freq(temp.d$b4la, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage")
Cirrhosis, liver damage
n % val%
No 1494 94.9 98.4
Yes 24 1.5 1.6
NA 57 3.6 NA
Total 1575 100.0 100.0 
  b4lb <- as.factor(d[,"b4lb"])
  new.d <- data.frame(new.d, b4lb)
  new.d <- apply_labels(new.d, b4lb = "Cirrhosis, liver damage age")
  temp.d <- data.frame (new.d, b4lb)  
  result<-questionr::freq(temp.d$b4lb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage Age")
Cirrhosis, liver damage Age
n % val%
3 1 0.1 5.9
42 1 0.1 5.9
45 2 0.1 11.8
51 2 0.1 11.8
54 1 0.1 5.9
55 2 0.1 11.8
60 3 0.2 17.6
63 1 0.1 5.9
64 1 0.1 5.9
65 1 0.1 5.9
69 1 0.1 5.9
73 1 0.1 5.9
NA 1558 98.9 NA
Total 1575 100.0 100.0 
# Arthritis
  b4ma <- as.factor(d[,"b4ma"])
  # Make "*" to NA
b4ma[which(b4ma=="*")]<-"NA"
  levels(b4ma) <- list(No="1",
                     Yes="2")
  b4ma <- ordered(b4ma, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ma)
  new.d <- apply_labels(new.d, b4ma = "Arthritis")
  temp.d <- data.frame (new.d, b4ma)  
  
  result<-questionr::freq(temp.d$b4ma, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis")
Arthritis
n % val%
No 1336 84.8 88.3
Yes 177 11.2 11.7
NA 62 3.9 NA
Total 1575 100.0 100.0 
  b4mb <- as.factor(d[,"b4mb"])
  new.d <- data.frame(new.d, b4mb)
  new.d <- apply_labels(new.d, b4mb = "Arthritis age")
  temp.d <- data.frame (new.d, b4mb)  
  result<-questionr::freq(temp.d$b4mb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis Age")
Arthritis Age
n % val%
2 1 0.1 0.7
20 2 0.1 1.3
22 1 0.1 0.7
24 1 0.1 0.7
25 3 0.2 2.0
30 7 0.4 4.7
33 1 0.1 0.7
35 2 0.1 1.3
36 1 0.1 0.7
39 1 0.1 0.7
4 1 0.1 0.7
40 8 0.5 5.3
41 1 0.1 0.7
42 2 0.1 1.3
43 1 0.1 0.7
45 6 0.4 4.0
46 1 0.1 0.7
47 3 0.2 2.0
48 1 0.1 0.7
50 13 0.8 8.7
51 3 0.2 2.0
52 1 0.1 0.7
53 3 0.2 2.0
55 12 0.8 8.0
56 3 0.2 2.0
57 2 0.1 1.3
58 6 0.4 4.0
60 18 1.1 12.0
61 3 0.2 2.0
62 3 0.2 2.0
63 3 0.2 2.0
64 2 0.1 1.3
65 13 0.8 8.7
66 2 0.1 1.3
67 4 0.3 2.7
68 2 0.1 1.3
69 1 0.1 0.7
7 1 0.1 0.7
70 6 0.4 4.0
71 2 0.1 1.3
72 1 0.1 0.7
75 1 0.1 0.7
78 1 0.1 0.7
NA 1425 90.5 NA
Total 1575 100.0 100.0 
# Dementia
  b4na <- as.factor(d[,"b4na"])
  # Make "*" to NA
b4na[which(b4na=="*")]<-"NA"
  levels(b4na) <- list(No="1",
                     Yes="2")
  b4na <- ordered(b4na, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4na)
  new.d <- apply_labels(new.d, b4na = "Dementia")
  temp.d <- data.frame (new.d, b4na)  
  
  result<-questionr::freq(temp.d$b4na, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia")
Dementia
n % val%
No 1493 94.8 98.4
Yes 25 1.6 1.6
NA 57 3.6 NA
Total 1575 100.0 100.0 
  b4nb <- as.factor(d[,"b4nb"])
  new.d <- data.frame(new.d, b4nb)
  new.d <- apply_labels(new.d, b4nb = "Dementia age")
  temp.d <- data.frame (new.d, b4nb)  
  result<-questionr::freq(temp.d$b4nb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia Age")
Dementia Age
n % val%
10 1 0.1 6.2
62 1 0.1 6.2
64 1 0.1 6.2
65 1 0.1 6.2
66 1 0.1 6.2
70 6 0.4 37.5
72 1 0.1 6.2
73 2 0.1 12.5
75 2 0.1 12.5
NA 1559 99.0 NA
Total 1575 100.0 100.0 
# Depression 
  b4oa <- as.factor(d[,"b4oa"])
  # Make "*" to NA
b4oa[which(b4oa=="*")]<-"NA"
  levels(b4oa) <- list(No="1",
                     Yes="2")
  b4oa <- ordered(b4oa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4oa)
  new.d <- apply_labels(new.d, b4oa = "Depression")
  temp.d <- data.frame (new.d, b4oa)  
  
  result<-questionr::freq(temp.d$b4oa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression")
Depression
n % val%
No 1330 84.4 87.6
Yes 188 11.9 12.4
NA 57 3.6 NA
Total 1575 100.0 100.0 
  b4ob <- as.factor(d[,"b4ob"])
  new.d <- data.frame(new.d, b4ob)
  new.d <- apply_labels(new.d, b4ob = "Depression age")
  temp.d <- data.frame (new.d, b4ob)  
  result<-questionr::freq(temp.d$b4ob, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression Age")
Depression Age
n % val%
1 1 0.1 0.7
17 1 0.1 0.7
18 1 0.1 0.7
19 1 0.1 0.7
20 1 0.1 0.7
21 3 0.2 2.0
25 1 0.1 0.7
26 2 0.1 1.3
28 3 0.2 2.0
29 1 0.1 0.7
32 3 0.2 2.0
33 1 0.1 0.7
35 2 0.1 1.3
36 2 0.1 1.3
37 3 0.2 2.0
38 2 0.1 1.3
4 1 0.1 0.7
40 7 0.4 4.6
41 1 0.1 0.7
42 2 0.1 1.3
43 1 0.1 0.7
44 2 0.1 1.3
45 6 0.4 3.9
46 2 0.1 1.3
47 4 0.3 2.6
48 4 0.3 2.6
49 3 0.2 2.0
50 13 0.8 8.5
51 1 0.1 0.7
52 5 0.3 3.3
53 3 0.2 2.0
54 1 0.1 0.7
55 7 0.4 4.6
56 5 0.3 3.3
57 2 0.1 1.3
58 5 0.3 3.3
59 2 0.1 1.3
60 8 0.5 5.2
61 3 0.2 2.0
62 3 0.2 2.0
63 3 0.2 2.0
64 3 0.2 2.0
65 7 0.4 4.6
66 3 0.2 2.0
67 3 0.2 2.0
68 5 0.3 3.3
69 1 0.1 0.7
7 1 0.1 0.7
70 2 0.1 1.3
72 1 0.1 0.7
73 1 0.1 0.7
8 1 0.1 0.7
91 1 0.1 0.7
96 1 0.1 0.7
98 1 0.1 0.7
NA 1422 90.3 NA
Total 1575 100.0 100.0 
# AIDS
  b4pa <- as.factor(d[,"b4pa"])
  # Make "*" to NA
b4pa[which(b4pa=="*")]<-"NA"
  levels(b4pa) <- list(No="1",
                     Yes="2")
  b4pa <- ordered(b4pa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4pa)
  new.d <- apply_labels(new.d, b4pa = "AIDS")
  temp.d <- data.frame (new.d, b4pa)  
  
  result<-questionr::freq(temp.d$b4pa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS")
AIDS
n % val%
No 1504 95.5 99.1
Yes 14 0.9 0.9
NA 57 3.6 NA
Total 1575 100.0 100.0 
  b4pb <- as.factor(d[,"b4pb"])
  new.d <- data.frame(new.d, b4pb)
  new.d <- apply_labels(new.d, b4pb = "AIDS age")
  temp.d <- data.frame (new.d, b4pb)  
  result<-questionr::freq(temp.d$b4pb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS Age")
AIDS Age
n % val%
35 1 0.1 10
38 1 0.1 10
39 1 0.1 10
40 1 0.1 10
50 1 0.1 10
51 1 0.1 10
59 1 0.1 10
60 1 0.1 10
69 1 0.1 10
90 1 0.1 10
NA 1565 99.4 NA
Total 1575 100.0 100 
# Other Cancer
  b4qa <- as.factor(d[,"b4qa"])
  # Make "*" to NA
b4qa[which(b4qa=="*")]<-"NA"
  levels(b4qa) <- list(No="1",
                     Yes="2")
  b4qa <- ordered(b4qa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4qa)
  new.d <- apply_labels(new.d, b4qa = "Other Cancer")
  temp.d <- data.frame (new.d, b4qa)  
  
  result<-questionr::freq(temp.d$b4qa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer")
Other Cancer
n % val%
No 1404 89.1 93.3
Yes 101 6.4 6.7
NA 70 4.4 NA
Total 1575 100.0 100.0 
  b4qb <- as.factor(d[,"b4qb"])
  new.d <- data.frame(new.d, b4qb)
  new.d <- apply_labels(new.d, b4qb = "Other Cancer age")
  temp.d <- data.frame (new.d, b4qb)  
  result<-questionr::freq(temp.d$b4qb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer Age")
Other Cancer Age
n % val%
16 1 0.1 1.1
18 1 0.1 1.1
3 1 0.1 1.1
39 1 0.1 1.1
40 1 0.1 1.1
43 1 0.1 1.1
45 1 0.1 1.1
47 1 0.1 1.1
49 4 0.3 4.5
50 1 0.1 1.1
51 1 0.1 1.1
52 1 0.1 1.1
54 3 0.2 3.4
55 5 0.3 5.6
56 5 0.3 5.6
57 2 0.1 2.2
58 4 0.3 4.5
59 1 0.1 1.1
60 10 0.6 11.2
61 4 0.3 4.5
62 2 0.1 2.2
63 4 0.3 4.5
64 4 0.3 4.5
65 3 0.2 3.4
66 1 0.1 1.1
67 2 0.1 2.2
68 2 0.1 2.2
69 4 0.3 4.5
70 4 0.3 4.5
72 3 0.2 3.4
73 1 0.1 1.1
74 5 0.3 5.6
75 2 0.1 2.2
76 1 0.1 1.1
77 1 0.1 1.1
78 1 0.1 1.1
NA 1486 94.3 NA
Total 1575 100.0 100.0

B4Q Other Cancer

b4qother <- d[,"b4qother"]
  new.d <- data.frame(new.d, b4qother)
  new.d <- apply_labels(new.d, b4qother = "b4qother")
  temp.d <- data.frame (new.d, b4qother)
result<-questionr::freq(temp.d$b4qother, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B4Q Other")
B4Q Other
n % val%
3rd stage lung cancer 1 0.1 1.1
Bladder 6 0.4 6.4
Bladder cancer. 1 0.1 1.1
Bladder. 1 0.1 1.1
Blood cancer 1 0.1 1.1
Bone cancer 1 0.1 1.1
CLL 2018/Lung cancer 1 0.1 1.1
CML Chronic Myeloid Leukemia 1 0.1 1.1
Colon 15 1.0 16.0
Colon cancer 4 0.3 4.3
Colon Cancer 1 0.1 1.1
Colon cancer. 1 0.1 1.1
Esophageal 1 0.1 1.1
Esophagus, lung cancer 1 0.1 1.1
HIV 1 0.1 1.1
In the family (call me). 1 0.1 1.1
Kidney 6 0.4 6.4
Kidney cancer 2 0.1 2.1
Kidney cancer in 2016 1 0.1 1.1
Kidney had kidney ablasion. 1 0.1 1.1
Left kidney 1 0.1 1.1
Left kidney was taken (believe to be cancerous) after taken and tested it was not cancerous. So I have only one now, right side. 1 0.1 1.1
Left kidney. 1 0.1 1.1
Liver had transplant 71 years 1 0.1 1.1
LMS 1 0.1 1.1
Lump in right side of neck 1 0.1 1.1
Lung 3 0.2 3.2
Lung caner 1 0.1 1.1
Lung, —- on kidney, prostate 1 0.1 1.1
Lung, esophagus 1 0.1 1.1
Lung/GIST tumor. 1 0.1 1.1
Lymphoma 1 0.1 1.1
MAL and Lymphoma 1 0.1 1.1
Malignant tumor in bladder. 1 0.1 1.1
Melanoma 1 0.1 1.1
Multiple myeloma 1 0.1 1.1
Multiple Myeloma 3 0.2 3.2
My left kidney was removed because a mass was found on it in April 2018, it was cancer, my kidney was removed on May 1, 2018. 1 0.1 1.1
Neuroendocrine (Carcinoma) 1 0.1 1.1
No 1 0.1 1.1
Non Hodgkin’s Lymphoma (CLL) 1 0.1 1.1
Non-Hodgkin’s lymphoma 1 0.1 1.1
Partial left kidney removal 1 0.1 1.1
Prostate 1 0.1 1.1
Rectal 1 0.1 1.1
Renal kidney remove left side 1 0.1 1.1
Results by Dr. Libby 2018 no cancer found! 1 0.1 1.1
Right kidney 1 0.1 1.1
Right vocal cord 1 0.1 1.1
Salivary gland cancer. 1 0.1 1.1
Skin cancer 2 0.1 2.1
Thank God, I don’t need more 1 0.1 1.1
Throat 3 0.2 3.2
Throat cancer, vocal chord cancer 1 0.1 1.1
Thyroid 2 0.1 2.1
Thyroid. 1 0.1 1.1
Tumor cancer 1 0.1 1.1
Waldenstrom Non Hodgkins Lymphoma 1 0.1 1.1
NA 1481 94.0 NA
Total 1575 100.0 100.0

B5: Routine care

  • B5. Where do you usually go for routine medical care (seeing a doctor for any reason, not just for cancer care)?
    • 1=Community health center or free clinic
    • 2=Hospital (not emergency)/ urgent care clinic
    • 3=Private doctor’s office
    • 4=Emergency room
    • 5=Veteran’s Affairs/VA
    • 6=Other type of location
  b5 <- as.factor(d[,"b5"])
# Make "*" to NA
b5[which(b5=="*")]<-"NA"
  levels(b5) <- list(Community_center_free_clinic="1",
                     Hospital_urgent_care_clinic="2",
                     Private_Dr_office="3",
                     ER="4",
                     VA="5",
                     Other="6")
  b5 <- ordered(b5, c("Community_center_free_clinic", "Hospital_urgent_care_clinic", "Private_Dr_office", "ER","VA","Other"))
  
  new.d <- data.frame(new.d, b5)
  new.d <- apply_labels(new.d, b5 = "routine medical care")
  temp.d <- data.frame (new.d, b5)  
  
  result<-questionr::freq(temp.d$b5 ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Community_center_free_clinic 99 6.3 7.2
Hospital_urgent_care_clinic 86 5.5 6.2
Private_Dr_office 951 60.4 68.7
ER 14 0.9 1.0
VA 210 13.3 15.2
Other 24 1.5 1.7
NA 191 12.1 NA
Total 1575 100.0 100.0

B5 Other: Routine care

b5other <- d[,"b5other"]
  new.d <- data.frame(new.d, b5other)
  new.d <- apply_labels(new.d, b5other = "b5other")
  temp.d <- data.frame (new.d, b5other)
result<-questionr::freq(temp.d$b5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B5 Other")
B5 Other
n % val%
Aenta 1 0.1 1.6
Albany health care center 1 0.1 1.6
Braselton Clinic, Braselton GA 1 0.1 1.6
Charleston SC Hinesville Cr. 1 0.1 1.6
Doctor affiliated with hospital 1 0.1 1.6
Dr. Alvin Griffin 1 0.1 1.6
Dr. Harper 1 0.1 1.6
Dr. Marvin Crawford (Piedmont) 1 0.1 1.6
Dr. Reed Laygo 1 0.1 1.6
Eagle Landing family practice 1 0.1 1.6
Emomory Health Care La Grange GA 1 0.1 1.6
Emor Health Care 1 0.1 1.6
Emory 1 0.1 1.6
Emory (PCP) 1 0.1 1.6
Emory Clinic 1 0.1 1.6
Emory Clinic. 1 0.1 1.6
Emory hospital transplant center 1 0.1 1.6
Emory Midtown 1 0.1 1.6
Emory-Neurological unit for Alzheimer’s 1 0.1 1.6
Eye Doctor/Eye sight associate 1 0.1 1.6
Family Health center 1 0.1 1.6
Family practice center 1 0.1 1.6
Fern Cave Medical Center 1 0.1 1.6
FPT, Jacksonville, FL 1 0.1 1.6
Grady 1 0.1 1.6
Grady Health System. 1 0.1 1.6
Grady Hospital HTI. 1 0.1 1.6
Grady Memorial Purple Pod 1 0.1 1.6
Heart doctor 1 0.1 1.6
HWY 138 Riverdale GA 30296 1 0.1 1.6
I’m retired from Army 1 0.1 1.6
JBACC 1 0.1 1.6
Kaiser Permanente 1 0.1 1.6
Medicate, Pulmonary doctor and urologist in my area 1 0.1 1.6
Mercer Medicine LLC. 1 0.1 1.6
Morehouse/Grady 1 0.1 1.6
My regular doctor 1 0.1 1.6
Palvillon Houston Health Care 1 0.1 1.6
PCP office. 1 0.1 1.6
PCP, Dr. Vannoy 1 0.1 1.6
Primary care doctor. 1 0.1 1.6
Primary care physician 1 0.1 1.6
Primary care-Dr. Williams 1 0.1 1.6
Primary doctor 1 0.1 1.6
Primary doctors Rober —- 1 0.1 1.6
Primary Michelle Cooke 1 0.1 1.6
Primary physician. 1 0.1 1.6
Private doctor is 1 0.1 1.6
Private doctors office 2200 Emery — Hill. 1 0.1 1.6
Private Dr. 1 0.1 1.6
Reg. Dr. follow up 1 0.1 1.6
Southwell Clinic Hwy 41 N, Tifton, GA 31794 1 0.1 1.6
Southwell Medical Clinic 1 0.1 1.6
Stopped going, mistreated 1 0.1 1.6
The Kauffman Clinic at Emory 1 0.1 1.6
Urgent care 1 0.1 1.6
Urgent care at VA facility. 1 0.1 1.6
Urgent care facility 1 0.1 1.6
Urologist/Diabetic Center/Hypertension doctor 1 0.1 1.6
Urology Specialist Clinic 1 0.1 1.6
Wellstar Medical Center 1 0.1 1.6
Wellstar/Northside. 1 0.1 1.6
West Georgia Medical Center Villa —- GA 1 0.1 1.6
NA 1512 96.0 NA
Total 1575 100.0 100.0

C1: Years lived at current address

  • C1. How many years have you lived in your current address?
    • 1=Less than 1 year
    • 2=1-5 years
    • 3=6-10 years
    • 4=11-15 years
    • 5=16-20 years
    • 6=21+ years
  c1 <- as.factor(d[,"c1"])
# Make "*" to NA
c1[which(c1=="*")]<-"NA"
  levels(c1) <- list(Less_than_1_year="1",
                     years_1_5="2",
                     years_6_10="3",
                     years_11_15="4",
                     years_16_20="5",
                     years_21_more="6")
  c1 <- ordered(c1, c("Less_than_1_year", "years_1_5", "years_6_10", "years_11_15","years_16_20","years_21_more"))
  
  new.d <- data.frame(new.d, c1)
  new.d <- apply_labels(new.d, c1 = "living period")
  temp.d <- data.frame (new.d, c1)  
  
  result<-questionr::freq(temp.d$c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Less_than_1_year 50 3.2 3.2 3.2 3.2
years_1_5 228 14.5 14.8 17.7 18.1
years_6_10 198 12.6 12.9 30.2 30.9
years_11_15 177 11.2 11.5 41.5 42.4
years_16_20 228 14.5 14.8 55.9 57.2
years_21_more 659 41.8 42.8 97.8 100.0
NA 35 2.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C2A: Feel safe walking in the neighborhood

    1. On average, I felt/feel safe walking in my neighborhood day or night.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2a1 <- as.factor(d[,"c2a1"])
# Make "*" to NA
c2a1[which(c2a1=="*")]<-"NA"
  levels(c2a1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a1 <- ordered(c2a1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a1)
  new.d <- apply_labels(new.d, c2a1 = "walk in the neighborhood-current")
  temp.d <- data.frame (new.d, c2a1)  
  
  c2a2 <- as.factor(d[,"c2a2"])
  # Make "*" to NA
c2a2[which(c2a2=="*")]<-"NA"
  levels(c2a2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a2 <- ordered(c2a2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a2)
  new.d <- apply_labels(new.d, c2a2 = "walk in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2a2) 
  
  c2a3 <- as.factor(d[,"c2a3"])
  # Make "*" to NA
c2a3[which(c2a3=="*")]<-"NA"
  levels(c2a3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a3 <- ordered(c2a3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a3)
  new.d <- apply_labels(new.d, c2a3 = "walk in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2a3)
  
  result<-questionr::freq(temp.d$c2a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 748 47.5 49.0 47.5 49.0
Agree 516 32.8 33.8 80.3 82.9
Neutral 167 10.6 11.0 90.9 93.8
Disagree 70 4.4 4.6 95.3 98.4
Strongly_Disagree 24 1.5 1.6 96.8 100.0
NA 50 3.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 629 39.9 43.5 39.9 43.5
Agree 526 33.4 36.4 73.3 79.8
Neutral 209 13.3 14.4 86.6 94.3
Disagree 58 3.7 4.0 90.3 98.3
Strongly_Disagree 25 1.6 1.7 91.9 100.0
NA 128 8.1 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 659 41.8 46.1 41.8 46.1
Agree 462 29.3 32.4 71.2 78.5
Neutral 199 12.6 13.9 83.8 92.4
Disagree 76 4.8 5.3 88.6 97.8
Strongly_Disagree 32 2.0 2.2 90.7 100.0
NA 147 9.3 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C2B: Violence

    1. Violence was/is not a problem in my neighborhood.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2b1 <- as.factor(d[,"c2b1"])
# Make "*" to NA
c2b1[which(c2b1=="*")]<-"NA"
  levels(c2b1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b1 <- ordered(c2b1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b1)
  new.d <- apply_labels(new.d, c2b1 = "Violence in the neighborhood-current")
  temp.d <- data.frame (new.d, c2b1)  
  
  c2b2 <- as.factor(d[,"c2b2"])
  # Make "*" to NA
c2b2[which(c2b2=="*")]<-"NA"
  levels(c2b2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b2 <- ordered(c2b2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b2)
  new.d <- apply_labels(new.d, c2b2 = "Violence in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2b2) 
  
  c2b3 <- as.factor(d[,"c2b3"])
  # Make "*" to NA
c2b3[which(c2b3=="*")]<-"NA"
  levels(c2b3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b3 <- ordered(c2b3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b3)
  new.d <- apply_labels(new.d, c2b3 = "Violence in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2b3)
  
  result<-questionr::freq(temp.d$c2b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 663 42.1 43.8 42.1 43.8
Agree 499 31.7 33.0 73.8 76.8
Neutral 197 12.5 13.0 86.3 89.8
Disagree 102 6.5 6.7 92.8 96.5
Strongly_Disagree 53 3.4 3.5 96.1 100.0
NA 61 3.9 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 527 33.5 36.7 33.5 36.7
Agree 515 32.7 35.8 66.2 72.5
Neutral 244 15.5 17.0 81.7 89.5
Disagree 102 6.5 7.1 88.1 96.6
Strongly_Disagree 49 3.1 3.4 91.2 100.0
NA 138 8.8 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 525 33.3 37.0 33.3 37.0
Agree 490 31.1 34.5 64.4 71.5
Neutral 227 14.4 16.0 78.9 87.5
Disagree 122 7.7 8.6 86.6 96.1
Strongly_Disagree 56 3.6 3.9 90.2 100.0
NA 155 9.8 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C2C: Safe from crime

    1. My neighborhood was/is safe from crime.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2c1 <- as.factor(d[,"c2c1"])
# Make "*" to NA
c2c1[which(c2c1=="*")]<-"NA"
  levels(c2c1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c1 <- ordered(c2c1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c1)
  new.d <- apply_labels(new.d, c2c1 = "safe from crime in the neighborhood-current")
  temp.d <- data.frame (new.d, c2c1)  
  
  c2c2 <- as.factor(d[,"c2c2"])
  # Make "*" to NA
c2c2[which(c2c2=="*")]<-"NA"
  levels(c2c2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c2 <- ordered(c2c2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c2)
  new.d <- apply_labels(new.d, c2c2 = "safe from crime in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2c2) 
  
  c2c3 <- as.factor(d[,"c2c3"])
  # Make "*" to NA
c2c3[which(c2c3=="*")]<-"NA"
  levels(c2c3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c3 <- ordered(c2c3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c3)
  new.d <- apply_labels(new.d, c2c3 = "safe from crime in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2c3)
  
  result<-questionr::freq(temp.d$c2c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 476 30.2 31.7 30.2 31.7
Agree 502 31.9 33.4 62.1 65.1
Neutral 320 20.3 21.3 82.4 86.4
Disagree 159 10.1 10.6 92.5 97.0
Strongly_Disagree 45 2.9 3.0 95.4 100.0
NA 73 4.6 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 405 25.7 28.7 25.7 28.7
Agree 492 31.2 34.8 57.0 63.5
Neutral 332 21.1 23.5 78.0 87.0
Disagree 143 9.1 10.1 87.1 97.1
Strongly_Disagree 41 2.6 2.9 89.7 100.0
NA 162 10.3 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 427 27.1 30.3 27.1 30.3
Agree 466 29.6 33.0 56.7 63.3
Neutral 304 19.3 21.5 76.0 84.8
Disagree 154 9.8 10.9 85.8 95.7
Strongly_Disagree 60 3.8 4.3 89.6 100.0
NA 164 10.4 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C3A: Traffic

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Traffic
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3a1 <- as.factor(d[,"c3a1"])
# Make "*" to NA
c3a1[which(c3a1=="*")]<-"NA"
  levels(c3a1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a1 <- ordered(c3a1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a1)
  new.d <- apply_labels(new.d, c3a1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3a1)  
  
  c3a2 <- as.factor(d[,"c3a2"])
  # Make "*" to NA
c3a2[which(c3a2=="*")]<-"NA"
  levels(c3a2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a2 <- ordered(c3a2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a2)
  new.d <- apply_labels(new.d, c3a2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3a2) 
  
  c3a3 <- as.factor(d[,"c3a3"])
  # Make "*" to NA
c3a3[which(c3a3=="*")]<-"NA"
  levels(c3a3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a3 <- ordered(c3a3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a3)
  new.d <- apply_labels(new.d, c3a3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3a3)
  
  result<-questionr::freq(temp.d$c3a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 1130 71.7 74.1 71.7 74.1
Somewhat_serious 238 15.1 15.6 86.9 89.8
Very_serious 85 5.4 5.6 92.3 95.3
Dont_know 71 4.5 4.7 96.8 100.0
NA 51 3.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 1077 68.4 73.9 68.4 73.9
Somewhat_serious 245 15.6 16.8 83.9 90.7
Very_serious 42 2.7 2.9 86.6 93.6
Dont_know 93 5.9 6.4 92.5 100.0
NA 118 7.5 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 1148 72.9 79.4 72.9 79.4
Somewhat_serious 140 8.9 9.7 81.8 89.1
Very_serious 27 1.7 1.9 83.5 91.0
Dont_know 130 8.3 9.0 91.7 100.0
NA 130 8.3 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C3B: Noise

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. A lot of noise
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3b1 <- as.factor(d[,"c3b1"])
# Make "*" to NA
c3b1[which(c3b1=="*")]<-"NA"
  levels(c3b1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b1 <- ordered(c3b1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b1)
  new.d <- apply_labels(new.d, c3b1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3b1)  
  
  c3b2 <- as.factor(d[,"c3b2"])
  # Make "*" to NA
c3b2[which(c3b2=="*")]<-"NA"
  levels(c3b2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b2 <- ordered(c3b2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b2)
  new.d <- apply_labels(new.d, c3b2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3b2) 
  
  c3b3 <- as.factor(d[,"c3b3"])
  # Make "*" to NA
c3b3[which(c3b3=="*")]<-"NA"
  levels(c3b3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b3 <- ordered(c3b3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b3)
  new.d <- apply_labels(new.d, c3b3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3b3)
  
  result<-questionr::freq(temp.d$c3b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 1277 81.1 84.9 81.1 84.9
Somewhat_serious 165 10.5 11.0 91.6 95.9
Very_serious 20 1.3 1.3 92.8 97.2
Dont_know 42 2.7 2.8 95.5 100.0
NA 71 4.5 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 1158 73.5 80.4 73.5 80.4
Somewhat_serious 202 12.8 14.0 86.3 94.4
Very_serious 26 1.7 1.8 88.0 96.2
Dont_know 55 3.5 3.8 91.5 100.0
NA 134 8.5 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 1144 72.6 80.0 72.6 80.0
Somewhat_serious 158 10.0 11.0 82.7 91.0
Very_serious 36 2.3 2.5 85.0 93.6
Dont_know 92 5.8 6.4 90.8 100.0
NA 145 9.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C3C: Trash and litter

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Trash and litter
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3c1 <- as.factor(d[,"c3c1"])
# Make "*" to NA
c3c1[which(c3c1=="*")]<-"NA"
  levels(c3c1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c1 <- ordered(c3c1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c1)
  new.d <- apply_labels(new.d, c3c1 = "Trash and litter-Current")
  temp.d <- data.frame (new.d, c3c1)  
  
  c3c2 <- as.factor(d[,"c3c2"])
  # Make "*" to NA
c3c2[which(c3c2=="*")]<-"NA"
  levels(c3c2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c2 <- ordered(c3c2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c2)
  new.d <- apply_labels(new.d, c3c2 = "Trash and litter-age 31 up")
  temp.d <- data.frame (new.d, c3c2) 
  
  c3c3 <- as.factor(d[,"c3c3"])
  # Make "*" to NA
c3c3[which(c3c3=="*")]<-"NA"
  levels(c3c3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c3 <- ordered(c3c3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c3)
  new.d <- apply_labels(new.d, c3c3 = "Trash and litter-Childhood or young")
  temp.d <- data.frame (new.d, c3c3)
  
  result<-questionr::freq(temp.d$c3c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 1296 82.3 86.0 82.3 86.0
Somewhat_serious 134 8.5 8.9 90.8 94.9
Very_serious 45 2.9 3.0 93.7 97.9
Dont_know 32 2.0 2.1 95.7 100.0
NA 68 4.3 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 1206 76.6 83.9 76.6 83.9
Somewhat_serious 150 9.5 10.4 86.1 94.3
Very_serious 30 1.9 2.1 88.0 96.4
Dont_know 52 3.3 3.6 91.3 100.0
NA 137 8.7 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 1175 74.6 81.8 74.6 81.8
Somewhat_serious 137 8.7 9.5 83.3 91.4
Very_serious 40 2.5 2.8 85.8 94.2
Dont_know 84 5.3 5.8 91.2 100.0
NA 139 8.8 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C3D: Too much light at night

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Too much light at night
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3d1 <- as.factor(d[,"c3d1"])
# Make "*" to NA
c3d1[which(c3d1=="*")]<-"NA"
  levels(c3d1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d1 <- ordered(c3d1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d1)
  new.d <- apply_labels(new.d, c3d1 = "Too much light at night-Current")
  temp.d <- data.frame (new.d, c3d1)  
  
  c3d2 <- as.factor(d[,"c3d2"])
  # Make "*" to NA
c3d2[which(c3d2=="*")]<-"NA"
  levels(c3d2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d2 <- ordered(c3d2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d2)
  new.d <- apply_labels(new.d, c3d2 = "Too much light at night-age 31 up")
  temp.d <- data.frame (new.d, c3d2) 
  
  c3d3 <- as.factor(d[,"c3d3"])
  # Make "*" to NA
c3d3[which(c3d3=="*")]<-"NA"
  levels(c3d3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d3 <- ordered(c3d3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d3)
  new.d <- apply_labels(new.d, c3d3 = "Too much light at night-Childhood or young")
  temp.d <- data.frame (new.d, c3d3)
  
  result<-questionr::freq(temp.d$c3d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 1393 88.4 92.7 88.4 92.7
Somewhat_serious 41 2.6 2.7 91.0 95.5
Very_serious 9 0.6 0.6 91.6 96.1
Dont_know 59 3.7 3.9 95.4 100.0
NA 73 4.6 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 1287 81.7 89.8 81.7 89.8
Somewhat_serious 57 3.6 4.0 85.3 93.8
Very_serious 12 0.8 0.8 86.1 94.6
Dont_know 77 4.9 5.4 91.0 100.0
NA 142 9.0 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 1249 79.3 87.2 79.3 87.2
Somewhat_serious 51 3.2 3.6 82.5 90.7
Very_serious 13 0.8 0.9 83.4 91.6
Dont_know 120 7.6 8.4 91.0 100.0
NA 142 9.0 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C4A: Neighbors talking outside

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did you see neighbors talking outside in the yard, on the street, at the corner park, etc.?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4a1 <- as.factor(d[,"c4a1"])
# Make "*" to NA
c4a1[which(c4a1=="*")]<-"NA"
  levels(c4a1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a1 <- ordered(c4a1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a1)
  new.d <- apply_labels(new.d, c4a1 = "Talk outside-Current")
  temp.d <- data.frame (new.d, c4a1)  
  
  c4a2 <- as.factor(d[,"c4a2"])
# Make "*" to NA
c4a2[which(c4a2=="*")]<-"NA" 
  levels(c4a2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a2 <- ordered(c4a2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a2)
  new.d <- apply_labels(new.d, c4a2 = "Talk outside-age 31 up")
  temp.d <- data.frame (new.d, c4a2) 
  
  c4a3 <- as.factor(d[,"c4a3"])
  # Make "*" to NA
c4a3[which(c4a3=="*")]<-"NA"
  levels(c4a3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a3 <- ordered(c4a3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a3)
  new.d <- apply_labels(new.d, c4a3 = "Talk outside-Childhood or young")
  temp.d <- data.frame (new.d, c4a3)
  
  result<-questionr::freq(temp.d$c4a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 510 32.4 33.4 32.4 33.4
Sometimes 679 43.1 44.4 75.5 77.8
Rarely_Never 309 19.6 20.2 95.1 98.0
Dont_know 31 2.0 2.0 97.1 100.0
NA 46 2.9 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 530 33.7 36.4 33.7 36.4
Sometimes 660 41.9 45.3 75.6 81.6
Rarely_Never 204 13.0 14.0 88.5 95.6
Dont_know 64 4.1 4.4 92.6 100.0
NA 117 7.4 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 785 49.8 54.2 49.8 54.2
Sometimes 410 26.0 28.3 75.9 82.5
Rarely_Never 151 9.6 10.4 85.5 93.0
Dont_know 102 6.5 7.0 91.9 100.0
NA 127 8.1 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C4B: Neighbors watch out for each other

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did neighbors watch out for each other, such as calling if they see a problem?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4b1 <- as.factor(d[,"c4b1"])
# Make "*" to NA
c4b1[which(c4b1=="*")]<-"NA"
  levels(c4b1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b1 <- ordered(c4b1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b1)
  new.d <- apply_labels(new.d, c4b1 = "watch out-Current")
  temp.d <- data.frame (new.d, c4b1)  
  
  c4b2 <- as.factor(d[,"c4b2"])
  # Make "*" to NA
c4b2[which(c4b2=="*")]<-"NA"
  levels(c4b2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b2 <- ordered(c4b2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b2)
  new.d <- apply_labels(new.d, c4b2 = "watch out-age 31 up")
  temp.d <- data.frame (new.d, c4b2) 
  
  c4b3 <- as.factor(d[,"c4b3"])
  # Make "*" to NA
c4b3[which(c4b3=="*")]<-"NA"
  levels(c4b3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b3 <- ordered(c4b3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b3)
  new.d <- apply_labels(new.d, c4b3 = "watch out-Childhood or young")
  temp.d <- data.frame (new.d, c4b3)
  
  result<-questionr::freq(temp.d$c4b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 735 46.7 49.1 46.7 49.1
Sometimes 443 28.1 29.6 74.8 78.6
Rarely_Never 211 13.4 14.1 88.2 92.7
Dont_know 109 6.9 7.3 95.1 100.0
NA 77 4.9 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 633 40.2 44.5 40.2 44.5
Sometimes 501 31.8 35.2 72.0 79.6
Rarely_Never 186 11.8 13.1 83.8 92.7
Dont_know 104 6.6 7.3 90.4 100.0
NA 151 9.6 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 825 52.4 58.3 52.4 58.3
Sometimes 317 20.1 22.4 72.5 80.7
Rarely_Never 136 8.6 9.6 81.1 90.3
Dont_know 137 8.7 9.7 89.8 100.0
NA 160 10.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C4C: Neighbors know by name

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you know by name?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4c1 <- as.factor(d[,"c4c1"])
# Make "*" to NA
c4c1[which(c4c1=="*")]<-"NA"
  levels(c4c1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c1 <- ordered(c4c1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c1)
  new.d <- apply_labels(new.d, c4c1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4c1)  
  
  c4c2 <- as.factor(d[,"c4c2"])
# Make "*" to NA
c4c2[which(c4c2=="*")]<-"NA"
  levels(c4c2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c2 <- ordered(c4c2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c2)
  new.d <- apply_labels(new.d, c4c2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4c2) 
  
  c4c3 <- as.factor(d[,"c4c3"])
# Make "*" to NA
c4c3[which(c4c3=="*")]<-"NA"
  levels(c4c3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c3 <- ordered(c4c3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c3)
  new.d <- apply_labels(new.d, c4c3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4c3)
  
  result<-questionr::freq(temp.d$c4c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 450 28.6 30.4 28.6 30.4
Sometimes 618 39.2 41.7 67.8 72.1
Rarely_Never 388 24.6 26.2 92.4 98.2
Dont_know 26 1.7 1.8 94.1 100.0
NA 93 5.9 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 477 30.3 33.9 30.3 33.9
Sometimes 578 36.7 41.1 67.0 75.0
Rarely_Never 305 19.4 21.7 86.3 96.7
Dont_know 47 3.0 3.3 89.3 100.0
NA 168 10.7 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 830 52.7 59.3 52.7 59.3
Sometimes 339 21.5 24.2 74.2 83.6
Rarely_Never 160 10.2 11.4 84.4 95.0
Dont_know 70 4.4 5.0 88.8 100.0
NA 176 11.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C4D: Friendly talks with neighbors

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you have a friendly talk with at least once a week?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4d1 <- as.factor(d[,"c4d1"])
# Make "*" to NA
c4d1[which(c4d1=="*")]<-"NA"
  levels(c4d1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d1 <- ordered(c4d1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d1)
  new.d <- apply_labels(new.d, c4d1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4d1)  
  
  c4d2 <- as.factor(d[,"c4d2"])
# Make "*" to NA
c4d2[which(c4d2=="*")]<-"NA"
  levels(c4d2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d2 <- ordered(c4d2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d2)
  new.d <- apply_labels(new.d, c4d2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4d2) 
  
  c4d3 <- as.factor(d[,"c4d3"])
  # Make "*" to NA
c4d3[which(c4d3=="*")]<-"NA"
  levels(c4d3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d3 <- ordered(c4d3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d3)
  new.d <- apply_labels(new.d, c4d3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4d3)
  
  result<-questionr::freq(temp.d$c4d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 184 11.7 12.4 11.7 12.4
Sometimes 547 34.7 36.7 46.4 49.1
Rarely_Never 726 46.1 48.8 92.5 97.9
Dont_know 32 2.0 2.1 94.5 100.0
NA 86 5.5 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 239 15.2 16.9 15.2 16.9
Sometimes 624 39.6 44.2 54.8 61.1
Rarely_Never 499 31.7 35.3 86.5 96.5
Dont_know 50 3.2 3.5 89.7 100.0
NA 163 10.3 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 578 36.7 41.0 36.7 41.0
Sometimes 444 28.2 31.5 64.9 72.4
Rarely_Never 298 18.9 21.1 83.8 93.6
Dont_know 91 5.8 6.4 89.6 100.0
NA 164 10.4 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

C4E: Ask neighbors for help

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors could you ask for help, such as to “borrow a cup of sugar” or some other small favor?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4e1 <- as.factor(d[,"c4e1"])
# Make "*" to NA
c4e1[which(c4e1=="*")]<-"NA"
  levels(c4e1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e1 <- ordered(c4e1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e1)
  new.d <- apply_labels(new.d, c4e1 = "ask for help-Current")
  temp.d <- data.frame (new.d, c4e1)  
  
  c4e2 <- as.factor(d[,"c4e2"])
# Make "*" to NA
c4e2[which(c4e2=="*")]<-"NA"
  levels(c4e2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e2 <- ordered(c4e2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e2)
  new.d <- apply_labels(new.d, c4e2 = "ask for help-age 31 up")
  temp.d <- data.frame (new.d, c4e2) 
  
  c4e3 <- as.factor(d[,"c4e3"])
  # Make "*" to NA
c4e3[which(c4e3=="*")]<-"NA"
  levels(c4e3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e3 <- ordered(c4e3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e3)
  new.d <- apply_labels(new.d, c4e3 = "ask for help-Childhood or young")
  temp.d <- data.frame (new.d, c4e3)
  
  result<-questionr::freq(temp.d$c4e1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 251 15.9 17.6 15.9 17.6
Sometimes 459 29.1 32.2 45.1 49.8
Rarely_Never 571 36.3 40.1 81.3 89.9
Dont_know 144 9.1 10.1 90.5 100.0
NA 150 9.5 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4e2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 274 17.4 20.2 17.4 20.2
Sometimes 497 31.6 36.7 49.0 56.9
Rarely_Never 448 28.4 33.1 77.4 90.0
Dont_know 135 8.6 10.0 86.0 100.0
NA 221 14.0 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4e3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 527 33.5 39.0 33.5 39.0
Sometimes 388 24.6 28.7 58.1 67.7
Rarely_Never 295 18.7 21.8 76.8 89.5
Dont_know 142 9.0 10.5 85.8 100.0
NA 223 14.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

D1: Treat you because of your race/ethnicity

  • D1. In the following questions, we are interested in your perceptions about the way other people have treated you because of your race/ethnicity or skin color.
      1. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
      1. For unfair reasons, have you ever not been hired for a job?
      1. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
      1. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
      1. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
      1. Have you ever been unfairly denied a bank loan?
      1. Have you ever been unfairly treated when getting medical care?
      • 1=No
      • 2=Yes
    • If yes, How stressful was this experience?
      • 1=Not at all
      • 2=A little
      • 3=Somewhat
      • 4=Extremely
# a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
  d1aa <- as.factor(d[,"d1aa"])
# Make "*" to NA
d1aa[which(d1aa=="*")]<-"NA"
  levels(d1aa) <- list(No="1",
                     Yes="2")
  d1aa <- ordered(d1aa, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1aa)
  new.d <- apply_labels(new.d, d1aa = "fired or denied a promotion")
  temp.d <- data.frame (new.d, d1aa)  
  
  d1ab <- as.factor(d[,"d1ab"])
# Make "*" to NA
d1ab[which(d1ab=="*")]<-"NA" 
  levels(d1ab) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1ab <- ordered(d1ab, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ab)
  new.d <- apply_labels(new.d, d1ab = "fired or denied a promotion-stressful")
  temp.d <- data.frame (new.d, d1ab)
  
  result<-questionr::freq(temp.d$d1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
")
a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 794 50.4 53
Yes 705 44.8 47
NA 76 4.8 NA
Total 1575 100.0 100 
  result<-questionr::freq(temp.d$d1ab,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How stressful was this experience?")
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 1575 100 NA 100 NA
Total 1575 100 100 100 100 
# b. For unfair reasons, have you ever not been hired for a job?
  d1ba <- as.factor(d[,"d1ba"])
  # Make "*" to NA
d1ba[which(d1ba=="*")]<-"NA"
  levels(d1ba) <- list(No="1",
                     Yes="2")
  d1ba <- ordered(d1ba, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ba)
  new.d <- apply_labels(new.d, d1ba = "not be hired")
  temp.d <- data.frame (new.d, d1ba)  
  
  d1bb <- as.factor(d[,"d1bb"])
  # Make "*" to NA
d1bb[which(d1bb=="*")]<-"NA"
  levels(d1bb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1bb <- ordered(d1bb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1bb)
  new.d <- apply_labels(new.d, d1bb = "not be hired-stressful")
  temp.d <- data.frame (new.d, d1bb)
  
  result<-questionr::freq(temp.d$d1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. For unfair reasons, have you ever not been hired for a job?")
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 957 60.8 64.9
Yes 517 32.8 35.1
NA 101 6.4 NA
Total 1575 100.0 100.0 
  result<-questionr::freq(temp.d$d1bb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How stressful was this experience?")
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 1575 100 NA 100 NA
Total 1575 100 100 100 100 
# c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
  d1ca <- as.factor(d[,"d1ca"])
  # Make "*" to NA
d1ca[which(d1ca=="*")]<-"NA"
  levels(d1ca) <- list(No="1",
                     Yes="2")
  d1ca <- ordered(d1ca, c( "No","Yes"))
  
  new.d <- data.frame(new.d, d1ca)
  new.d <- apply_labels(new.d, d1ca = "By police")
  temp.d <- data.frame (new.d, d1ca)  
  
  result<-questionr::freq(temp.d$d1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?")
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 905 57.5 60.5
Yes 590 37.5 39.5
NA 80 5.1 NA
Total 1575 100.0 100.0 
  d1cb <- as.factor(d[,"d1cb"])
  # Make "*" to NA
d1cb[which(d1cb=="*")]<-"NA"
  levels(d1cb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1cb <- ordered(d1cb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1cb)
  new.d <- apply_labels(new.d, d1cb = "By police-stressful")
  temp.d <- data.frame (new.d, d1cb)
  result<-questionr::freq(temp.d$d1cb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. If yes, How stressful was this experience?")
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 1575 100 NA 100 NA
Total 1575 100 100 100 100 
# d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
  d1da <- as.factor(d[,"d1da"])
  # Make "*" to NA
d1da[which(d1da=="*")]<-"NA"
  levels(d1da) <- list(No="1",
                     Yes="2")
  d1da <- ordered(d1da, c( "No","Yes"))
  
  new.d <- data.frame(new.d, d1da)
  new.d <- apply_labels(new.d, d1da = "unfair education")
  temp.d <- data.frame (new.d, d1da)  
  
  result<-questionr::freq(temp.d$d1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?")
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 1272 80.8 85.2
Yes 221 14.0 14.8
NA 82 5.2 NA
Total 1575 100.0 100.0 
  d1db <- as.factor(d[,"d1db"])
  # Make "*" to NA
d1db[which(d1db=="*")]<-"NA"
  levels(d1db) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1db <- ordered(d1db, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1db)
  new.d <- apply_labels(new.d, d1db = "unfair education-stressful")
  temp.d <- data.frame (new.d, d1db)
  result<-questionr::freq(temp.d$d1db,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. If yes, How stressful was this experience?")
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 1575 100 NA 100 NA
Total 1575 100 100 100 100 
# e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
  d1ea <- as.factor(d[,"d1ea"])
  # Make "*" to NA
d1ea[which(d1ea=="*")]<-"NA"
  levels(d1ea) <- list(No="1",
                     Yes="2")
  d1ea <- ordered(d1ea, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ea)
  new.d <- apply_labels(new.d, d1ea = "refuse to sell or rent")
  temp.d <- data.frame (new.d, d1ea)  
  
  result<-questionr::freq(temp.d$d1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?")
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 1331 84.5 88.2
Yes 178 11.3 11.8
NA 66 4.2 NA
Total 1575 100.0 100.0 
  d1eb <- as.factor(d[,"d1eb"])
  # Make "*" to NA
d1eb[which(d1eb=="*")]<-"NA"
  levels(d1eb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1eb <- ordered(d1eb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1eb)
  new.d <- apply_labels(new.d, d1eb = "refuse to sell or rent-stressful")
  temp.d <- data.frame (new.d, d1eb)
  result<-questionr::freq(temp.d$d1eb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. If yes, How stressful was this experience?")
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 1575 100 NA 100 NA
Total 1575 100 100 100 100 
# f.   Have   you   ever   been   unfairly denied a bank loan?
  d1fa <- as.factor(d[,"d1fa"])
  # Make "*" to NA
d1fa[which(d1fa=="*")]<-"NA"
  levels(d1fa) <- list(No="1",
                     Yes="2")
  d1fa <- ordered(d1fa, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1fa)
  new.d <- apply_labels(new.d, d1fa = "Bank loan")
  temp.d <- data.frame (new.d, d1fa)  
  
  result<-questionr::freq(temp.d$d1fa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. Have you ever been unfairly denied a bank loan?")
f. Have you ever been unfairly denied a bank loan?
n % val%
No 1069 67.9 71.8
Yes 419 26.6 28.2
NA 87 5.5 NA
Total 1575 100.0 100.0 
  d1fb <- as.factor(d[,"d1fb"])
  # Make "*" to NA
d1fb[which(d1fb=="*")]<-"NA"
  levels(d1fb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1fb <- ordered(d1fb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1fb)
  new.d <- apply_labels(new.d, d1fb = "Bank loan-stressful")
  temp.d <- data.frame (new.d, d1fb)
  result<-questionr::freq(temp.d$d1fb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. If yes, How stressful was this experience?")
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 1575 100 NA 100 NA
Total 1575 100 100 100 100 
# g.   Have   you   ever   been   unfairly treated when getting medical care?
  d1ga <- as.factor(d[,"d1ga"])
  # Make "*" to NA
d1ga[which(d1ga=="*")]<-"NA"
  levels(d1ga) <- list(No="1",
                     Yes="2")
  d1ga <- ordered(d1ga, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ga)
  new.d <- apply_labels(new.d, d1ga = "unfair medical care")
  temp.d <- data.frame (new.d, d1ga)  
  
  result<-questionr::freq(temp.d$d1ga,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Have you ever been unfairly treated when getting medical care?")
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 1285 81.6 86.4
Yes 203 12.9 13.6
NA 87 5.5 NA
Total 1575 100.0 100.0 
  d1gb <- as.factor(d[,"d1gb"])
  # Make "*" to NA
d1gb[which(d1gb=="*")]<-"NA"
  levels(d1gb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1gb <- ordered(d1gb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1gb)
  new.d <- apply_labels(new.d, d1gb = "unfair medical care-stressful")
  temp.d <- data.frame (new.d, d1gb)
  result<-questionr::freq(temp.d$d1gb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. If yes, How stressful was this experience?")
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 1575 100 NA 100 NA
Total 1575 100 100 100 100

D2: Medical Mistrust

  • D2. These next questions are about your current feelings or perceptions regarding healthcare organizations (places where you might get healthcare, like a hospital or clinic). Indicate your level of agreement or disagreement with each statement.
# a. Patients have sometimes been deceived or misled at hospitals.
  d2a <- as.factor(d[,"d2a"])
# Make "*" to NA
d2a[which(d2a=="*")]<-"NA"
  levels(d2a) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2a <- ordered(d2a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2a)
  new.d <- apply_labels(new.d, d2a = "deceived or misled")
  temp.d <- data.frame (new.d, d2a)  
  
  result<-questionr::freq(temp.d$d2a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Patients have sometimes been deceived or misled at hospitals.")
a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 206 13.1 13.8
Somewhat_Agree 654 41.5 43.9
Somewhat_Disagree 357 22.7 24.0
Strongly_Disagree 273 17.3 18.3
NA 85 5.4 NA
Total 1575 100.0 100.0 
# b. Hospitals often want to know more about your personal affairs or business than they really need to know.
  d2b <- as.factor(d[,"d2b"])
# Make "*" to NA
d2b[which(d2b=="*")]<-"NA"
  levels(d2b) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2b <- ordered(d2b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2b)
  new.d <- apply_labels(new.d, d2b = "personal affairs")
  temp.d <- data.frame (new.d, d2b)  
  
  result<-questionr::freq(temp.d$d2b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Hospitals often want to know more about your personal affairs or business than they really need to know.")
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 224 14.2 15.0
Somewhat_Agree 561 35.6 37.6
Somewhat_Disagree 422 26.8 28.3
Strongly_Disagree 285 18.1 19.1
NA 83 5.3 NA
Total 1575 100.0 100.0 
# c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
  d2c <- as.factor(d[,"d2c"])
# Make "*" to NA
d2c[which(d2c=="*")]<-"NA"
  levels(d2c) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2c <- ordered(d2c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2c)
  new.d <- apply_labels(new.d, d2c = "harmful experiments")
  temp.d <- data.frame (new.d, d2c)  
  
  result<-questionr::freq(temp.d$d2c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Hospitals have sometimes done harmful experiments on patients without their knowledge.")
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 267 17.0 18.4
Somewhat_Agree 496 31.5 34.3
Somewhat_Disagree 385 24.4 26.6
Strongly_Disagree 300 19.0 20.7
NA 127 8.1 NA
Total 1575 100.0 100.0 
# d. Rich patients receive better care at hospitals than poor patients.
  d2d <- as.factor(d[,"d2d"])
# Make "*" to NA
d2d[which(d2d=="*")]<-"NA"
  levels(d2d) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2d <- ordered(d2d, c( "Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2d)
  new.d <- apply_labels(new.d, d2d = "Rich patients better care")
  temp.d <- data.frame (new.d, d2d)  
  
  result<-questionr::freq(temp.d$d2d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Rich patients receive better care at hospitals than poor patients.")
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 796 50.5 54.2
Somewhat_Agree 379 24.1 25.8
Somewhat_Disagree 152 9.7 10.4
Strongly_Disagree 141 9.0 9.6
NA 107 6.8 NA
Total 1575 100.0 100.0 
# e. Male patients receive better care at hospitals than female patients.
  d2e <- as.factor(d[,"d2e"])
# Make "*" to NA
d2e[which(d2e=="*")]<-"NA"
  levels(d2e) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2e <- ordered(d2e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2e)
  new.d <- apply_labels(new.d, d2e = "Male patients better care")
  temp.d <- data.frame (new.d, d2e)  
  
  result<-questionr::freq(temp.d$d2e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Male patients receive better care at hospitals than female patients.")
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 51 3.2 3.5
Somewhat_Agree 206 13.1 14.2
Somewhat_Disagree 621 39.4 42.9
Strongly_Disagree 568 36.1 39.3
NA 129 8.2 NA
Total 1575 100.0 100.0

D3A: Treated with less respect

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been treated with less respect than other people
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3a1 <- as.factor(d[,"d3a1"])
# Make "*" to NA
d3a1[which(d3a1=="*")]<-"NA"
  levels(d3a1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a1 <- ordered(d3a1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a1)
  new.d <- apply_labels(new.d, d3a1 = "less respect-current")
  temp.d <- data.frame (new.d, d3a1)  
  
  result<-questionr::freq(temp.d$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 499 31.7 32.7
Rarely 443 28.1 29.0
Sometimes 519 33.0 34.0
Often 65 4.1 4.3
NA 49 3.1 NA
Total 1575 100.0 100.0 
#2
  d3a2 <- as.factor(d[,"d3a2"])
# Make "*" to NA
d3a2[which(d3a2=="*")]<-"NA"
  levels(d3a2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a2 <- ordered(d3a2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a2)
  new.d <- apply_labels(new.d, d3a2 = "less respect-31 up")
  temp.d <- data.frame (new.d, d3a2)  
  
  result<-questionr::freq(temp.d$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 384 24.4 26.7
Rarely 426 27.0 29.6
Sometimes 539 34.2 37.4
Often 91 5.8 6.3
NA 135 8.6 NA
Total 1575 100.0 100.0 
#3
  d3a3 <- as.factor(d[,"d3a3"])
  # Make "*" to NA
d3a3[which(d3a3=="*")]<-"NA"
  levels(d3a3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a3 <- ordered(d3a3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a3)
  new.d <- apply_labels(new.d, d3a3 = "less respect-child or young")
  temp.d <- data.frame (new.d, d3a3)  
  
  result<-questionr::freq(temp.d$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 344 21.8 24.2
Rarely 319 20.3 22.5
Sometimes 516 32.8 36.3
Often 241 15.3 17.0
NA 155 9.8 NA
Total 1575 100.0 100.0

D3B: Received poorer service

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have received poorer service than other people at restaurants or stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3b1 <- as.factor(d[,"d3b1"])
# Make "*" to NA
d3b1[which(d3b1=="*")]<-"NA"
  levels(d3b1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b1 <- ordered(d3b1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b1)
  new.d <- apply_labels(new.d, d3b1 = "poorer service-current")
  temp.d <- data.frame (new.d, d3b1)  
  
  result<-questionr::freq(temp.d$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 345 21.9 22.7
Rarely 504 32.0 33.2
Sometimes 607 38.5 40.0
Often 63 4.0 4.1
NA 56 3.6 NA
Total 1575 100.0 100.0 
#2
  d3b2 <- as.factor(d[,"d3b2"])
  # Make "*" to NA
d3b2[which(d3b2=="*")]<-"NA"
  levels(d3b2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b2 <- ordered(d3b2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b2)
  new.d <- apply_labels(new.d, d3b2 = "poorer service-31 up")
  temp.d <- data.frame (new.d, d3b2)  
  
  result<-questionr::freq(temp.d$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 282 17.9 19.7
Rarely 417 26.5 29.1
Sometimes 646 41.0 45.1
Often 88 5.6 6.1
NA 142 9.0 NA
Total 1575 100.0 100.0 
#3
  d3b3 <- as.factor(d[,"d3b3"])
  # Make "*" to NA
d3b3[which(d3b3=="*")]<-"NA"
  levels(d3b3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b3 <- ordered(d3b3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b3)
  new.d <- apply_labels(new.d, d3b3 = "poorer service-child or young")
  temp.d <- data.frame (new.d, d3b3)  
  
  result<-questionr::freq(temp.d$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 288 18.3 20.4
Rarely 308 19.6 21.8
Sometimes 562 35.7 39.8
Often 253 16.1 17.9
NA 164 10.4 NA
Total 1575 100.0 100.0

D3C: Think you are not smart

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are not smart
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3c1 <- as.factor(d[,"d3c1"])
# Make "*" to NA
d3c1[which(d3c1=="*")]<-"NA"
  levels(d3c1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c1 <- ordered(d3c1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c1)
  new.d <- apply_labels(new.d, d3c1 = "think you are not smart-current")
  temp.d <- data.frame (new.d, d3c1)  
  
  result<-questionr::freq(temp.d$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 453 28.8 30.1
Rarely 460 29.2 30.6
Sometimes 476 30.2 31.6
Often 115 7.3 7.6
NA 71 4.5 NA
Total 1575 100.0 100.0 
#2
  d3c2 <- as.factor(d[,"d3c2"])
# Make "*" to NA
d3c2[which(d3c2=="*")]<-"NA"
  levels(d3c2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c2 <- ordered(d3c2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c2)
  new.d <- apply_labels(new.d, d3c2 = "think you are not smart-31 up")
  temp.d <- data.frame (new.d, d3c2)  
  
  result<-questionr::freq(temp.d$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 366 23.2 25.8
Rarely 461 29.3 32.5
Sometimes 477 30.3 33.6
Often 114 7.2 8.0
NA 157 10.0 NA
Total 1575 100.0 100.0 
#3
  d3c3 <- as.factor(d[,"d3c3"])
  # Make "*" to NA
d3c3[which(d3c3=="*")]<-"NA"
  levels(d3c3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c3 <- ordered(d3c3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c3)
  new.d <- apply_labels(new.d, d3c3 = "think you are not smart-child or young")
  temp.d <- data.frame (new.d, d3c3)  
  
  result<-questionr::freq(temp.d$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 337 21.4 23.9
Rarely 346 22.0 24.5
Sometimes 526 33.4 37.3
Often 201 12.8 14.3
NA 165 10.5 NA
Total 1575 100.0 100.0

D3D: Be afraid of you

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they are afraid of you
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3d1 <- as.factor(d[,"d3d1"])
# Make "*" to NA
d3d1[which(d3d1=="*")]<-"NA"
  levels(d3d1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d1 <- ordered(d3d1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d1)
  new.d <- apply_labels(new.d, d3d1 = "be afraid of you-current")
  temp.d <- data.frame (new.d, d3d1)  
  
  result<-questionr::freq(temp.d$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 656 41.7 43.0
Rarely 424 26.9 27.8
Sometimes 373 23.7 24.5
Often 71 4.5 4.7
NA 51 3.2 NA
Total 1575 100.0 100.0 
#2
  d3d2 <- as.factor(d[,"d3d2"])
  # Make "*" to NA
d3d2[which(d3d2=="*")]<-"NA"
  levels(d3d2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d2 <- ordered(d3d2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d2)
  new.d <- apply_labels(new.d, d3d2 = "be afraid of you-31 up")
  temp.d <- data.frame (new.d, d3d2)  
  
  result<-questionr::freq(temp.d$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 536 34.0 37.5
Rarely 403 25.6 28.2
Sometimes 405 25.7 28.3
Often 86 5.5 6.0
NA 145 9.2 NA
Total 1575 100.0 100.0 
#3
  d3d3 <- as.factor(d[,"d3d3"])
  # Make "*" to NA
d3d3[which(d3d3=="*")]<-"NA"
  levels(d3d3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d3 <- ordered(d3d3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d3)
  new.d <- apply_labels(new.d, d3d3 = "be afraid of you-child or young")
  temp.d <- data.frame (new.d, d3d3)  
  
  result<-questionr::freq(temp.d$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 557 35.4 39.3
Rarely 360 22.9 25.4
Sometimes 374 23.7 26.4
Often 127 8.1 9.0
NA 157 10.0 NA
Total 1575 100.0 100.0

D3E: Think you are dishonest

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are dishonest
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3e1 <- as.factor(d[,"d3e1"])
# Make "*" to NA
d3e1[which(d3e1=="*")]<-"NA"
  levels(d3e1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e1 <- ordered(d3e1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e1)
  new.d <- apply_labels(new.d, d3e1 = "think you are dishonest-current")
  temp.d <- data.frame (new.d, d3e1)  
  
  result<-questionr::freq(temp.d$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 734 46.6 48.5
Rarely 411 26.1 27.2
Sometimes 311 19.7 20.6
Often 57 3.6 3.8
NA 62 3.9 NA
Total 1575 100.0 100.0 
#2
  d3e2 <- as.factor(d[,"d3e2"])
  # Make "*" to NA
d3e2[which(d3e2=="*")]<-"NA"
  levels(d3e2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e2 <- ordered(d3e2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e2)
  new.d <- apply_labels(new.d, d3e2 = "think you are dishonest-31 up")
  temp.d <- data.frame (new.d, d3e2)  
  
  result<-questionr::freq(temp.d$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 609 38.7 42.9
Rarely 406 25.8 28.6
Sometimes 337 21.4 23.7
Often 68 4.3 4.8
NA 155 9.8 NA
Total 1575 100.0 100.0 
#3
  d3e3 <- as.factor(d[,"d3e3"])
  # Make "*" to NA
d3e3[which(d3e3=="*")]<-"NA"
  levels(d3e3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e3 <- ordered(d3e3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e3)
  new.d <- apply_labels(new.d, d3e3 = "think you are dishonest-child or young")
  temp.d <- data.frame (new.d, d3e3)  
  
  result<-questionr::freq(temp.d$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 595 37.8 42.3
Rarely 332 21.1 23.6
Sometimes 344 21.8 24.5
Often 135 8.6 9.6
NA 169 10.7 NA
Total 1575 100.0 100.0

D3F: Better than you

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they’re better than you are
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3f1 <- as.factor(d[,"d3f1"])
# Make "*" to NA
d3f1[which(d3f1=="*")]<-"NA"
  levels(d3f1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f1 <- ordered(d3f1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f1)
  new.d <- apply_labels(new.d, d3f1 = "better than you-current")
  temp.d <- data.frame (new.d, d3f1)  
  
  result<-questionr::freq(temp.d$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 260 16.5 17.2
Rarely 433 27.5 28.6
Sometimes 661 42.0 43.6
Often 162 10.3 10.7
NA 59 3.7 NA
Total 1575 100.0 100.0 
#2
  d3f2 <- as.factor(d[,"d3f2"])
  # Make "*" to NA
d3f2[which(d3f2=="*")]<-"NA"
  levels(d3f2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f2 <- ordered(d3f2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f2)
  new.d <- apply_labels(new.d, d3f2 = "better than you-31 up")
  temp.d <- data.frame (new.d, d3f2)  
  
  result<-questionr::freq(temp.d$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 203 12.9 14.2
Rarely 373 23.7 26.0
Sometimes 678 43.0 47.3
Often 180 11.4 12.6
NA 141 9.0 NA
Total 1575 100.0 100.0 
#3
  d3f3 <- as.factor(d[,"d3f3"])
# Make "*" to NA
d3f3[which(d3f3=="*")]<-"NA"
  levels(d3f3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f3 <- ordered(d3f3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f3)
  new.d <- apply_labels(new.d, d3f3 = "better than you-child or young")
  temp.d <- data.frame (new.d, d3f3)  
  
  result<-questionr::freq(temp.d$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 195 12.4 13.8
Rarely 285 18.1 20.1
Sometimes 629 39.9 44.4
Often 308 19.6 21.7
NA 158 10.0 NA
Total 1575 100.0 100.0

D3G: Insulted

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been called names or insulted
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3g1 <- as.factor(d[,"d3g1"])
# Make "*" to NA
d3g1[which(d3g1=="*")]<-"NA"
  levels(d3g1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g1 <- ordered(d3g1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g1)
  new.d <- apply_labels(new.d, d3g1 = "called names or insulted-current")
  temp.d <- data.frame (new.d, d3g1)  
  
  result<-questionr::freq(temp.d$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 576 36.6 38.2
Rarely 494 31.4 32.8
Sometimes 380 24.1 25.2
Often 58 3.7 3.8
NA 67 4.3 NA
Total 1575 100.0 100.0 
#2
  d3g2 <- as.factor(d[,"d3g2"])
  # Make "*" to NA
d3g2[which(d3g2=="*")]<-"NA"
  levels(d3g2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g2 <- ordered(d3g2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g2)
  new.d <- apply_labels(new.d, d3g2 = "called names or insulted-31 up")
  temp.d <- data.frame (new.d, d3g2)  
  
  result<-questionr::freq(temp.d$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 408 25.9 28.6
Rarely 498 31.6 34.9
Sometimes 448 28.4 31.4
Often 73 4.6 5.1
NA 148 9.4 NA
Total 1575 100.0 100.0 
#3
  d3g3 <- as.factor(d[,"d3g3"])
  # Make "*" to NA
d3g3[which(d3g3=="*")]<-"NA"
  levels(d3g3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g3 <- ordered(d3g3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g3)
  new.d <- apply_labels(new.d, d3g3 = "called names or insulted-child or young")
  temp.d <- data.frame (new.d, d3g3)  
  
  result<-questionr::freq(temp.d$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 279 17.7 19.9
Rarely 396 25.1 28.2
Sometimes 517 32.8 36.8
Often 213 13.5 15.2
NA 170 10.8 NA
Total 1575 100.0 100.0

D3H: Threatened or harassed

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been threatened or harassed
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3h1 <- as.factor(d[,"d3h1"])
# Make "*" to NA
d3h1[which(d3h1=="*")]<-"NA"
  levels(d3h1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h1 <- ordered(d3h1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h1)
  new.d <- apply_labels(new.d, d3h1 = "threatened or harassed-current")
  temp.d <- data.frame (new.d, d3h1)  
  
  result<-questionr::freq(temp.d$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 891 56.6 59.1
Rarely 396 25.1 26.3
Sometimes 203 12.9 13.5
Often 18 1.1 1.2
NA 67 4.3 NA
Total 1575 100.0 100.0 
#2
  d3h2 <- as.factor(d[,"d3h2"])
  # Make "*" to NA
d3h2[which(d3e1=="*")]<-"NA"
  levels(d3h2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h2 <- ordered(d3h2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h2)
  new.d <- apply_labels(new.d, d3h2 = "threatened or harassed-31 up")
  temp.d <- data.frame (new.d, d3h2)  
  
  result<-questionr::freq(temp.d$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 680 43.2 47.8
Rarely 449 28.5 31.6
Sometimes 261 16.6 18.3
Often 33 2.1 2.3
NA 152 9.7 NA
Total 1575 100.0 100.0 
#3
  d3h3 <- as.factor(d[,"d3h3"])
  # Make "*" to NA
d3h3[which(d3h3=="*")]<-"NA"
  levels(d3h3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h3 <- ordered(d3h3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h3)
  new.d <- apply_labels(new.d, d3h3 = "threatened or harassed-child or young")
  temp.d <- data.frame (new.d, d3h3)  
  
  result<-questionr::freq(temp.d$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 536 34.0 38.1
Rarely 385 24.4 27.4
Sometimes 378 24.0 26.9
Often 108 6.9 7.7
NA 168 10.7 NA
Total 1575 100.0 100.0

D3I: Followed around in stores

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been followed around in stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3i1 <- as.factor(d[,"d3i1"])
# Make "*" to NA
d3i1[which(d3e1=="*")]<-"NA"
  levels(d3i1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i1 <- ordered(d3i1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i1)
  new.d <- apply_labels(new.d, d3i1 = "be followed-current")
  temp.d <- data.frame (new.d, d3i1)  
  
  result<-questionr::freq(temp.d$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 596 37.8 39.5
Rarely 418 26.5 27.7
Sometimes 400 25.4 26.5
Often 94 6.0 6.2
NA 67 4.3 NA
Total 1575 100.0 100.0 
#2
  d3i2 <- as.factor(d[,"d3i2"])
  # Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
  levels(d3i2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i2 <- ordered(d3i2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i2)
  new.d <- apply_labels(new.d, d3i2 = "be followed-31 up")
  temp.d <- data.frame (new.d, d3i2)  
  
  result<-questionr::freq(temp.d$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 449 28.5 31.6
Rarely 377 23.9 26.5
Sometimes 474 30.1 33.3
Often 123 7.8 8.6
NA 152 9.7 NA
Total 1575 100.0 100.0 
#3
  d3i3 <- as.factor(d[,"d3i3"])
  # Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
  levels(d3i3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i3 <- ordered(d3i3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i3)
  new.d <- apply_labels(new.d, d3i3 = "be followed-child or young")
  temp.d <- data.frame (new.d, d3i3)  
  
  result<-questionr::freq(temp.d$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 373 23.7 26.4
Rarely 294 18.7 20.8
Sometimes 470 29.8 33.3
Often 275 17.5 19.5
NA 163 10.3 NA
Total 1575 100.0 100.0

D3J: How stressful

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. How stressful has any of the above experience (a-i) of unfair treatment usually been for you?
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3j1 <- as.factor(d[,"d3j1"])
# Make "*" to NA
d3j1[which(d3j1=="*")]<-"NA"
  levels(d3j1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j1 <- ordered(d3j1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j1)
  new.d <- apply_labels(new.d, d3j1 = "How stressful-current")
  temp.d <- data.frame (new.d, d3j1)  
  
  result<-questionr::freq(temp.d$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 697 44.3 46.3
Rarely 505 32.1 33.6
Sometimes 245 15.6 16.3
Often 58 3.7 3.9
NA 70 4.4 NA
Total 1575 100.0 100.0 
#2
  d3j2 <- as.factor(d[,"d3j2"])
  # Make "*" to NA
d3j2[which(d3j2=="*")]<-"NA"
  levels(d3j2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j2 <- ordered(d3j2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j2)
  new.d <- apply_labels(new.d, d3j2 = "How stressful-31 up")
  temp.d <- data.frame (new.d, d3j2)  
  
  result<-questionr::freq(temp.d$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 550 34.9 38.7
Rarely 500 31.7 35.2
Sometimes 305 19.4 21.5
Often 65 4.1 4.6
NA 155 9.8 NA
Total 1575 100.0 100.0 
#3
  d3j3 <- as.factor(d[,"d3j3"])
  # Make "*" to NA
d3j3[which(d3j3=="*")]<-"NA"
  levels(d3j3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j3 <- ordered(d3j3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j3)
  new.d <- apply_labels(new.d, d3j3 = "How stressful-child or young")
  temp.d <- data.frame (new.d, d3j3)  
  
  result<-questionr::freq(temp.d$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 491 31.2 34.9
Rarely 434 27.6 30.8
Sometimes 327 20.8 23.2
Often 155 9.8 11.0
NA 168 10.7 NA
Total 1575 100.0 100.0

D4: How you currently see yourself

  • D4. These statements are about how you currently see yourself. Indicate your level of agreement or disagreement with each statement.
      1. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
      1. Once you make up your mind to do something, you stay with it until the job is completely done.
      1. You like doing things that other people thought could not be done.
      1. When things don’t go the way you want them to, that just makes you work even harder.
      1. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
      1. It’s not always easy, but you manage to find a way to do the things you really need to get done.
      1. Very seldom have you been disappointed by the results of your hard work.
      1. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
      1. In the past, even when things got really tough, you never lost sight of your goals.
      1. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
      1. You don’t let your personal feelings get in the way of doing a job.
      1. Hard work has really helped you to get ahead in life.
      • 1=Strongly Agree
      • 2=Somewhat Agree
      • 3=Somewhat Disagree
      • 4=Strongly Disagree
# a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
  d4a <- as.factor(d[,"d4a"])
# Make "*" to NA
d4a[which(d4a=="*")]<-"NA"
  levels(d4a) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4a <- ordered(d4a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4a)
  new.d <- apply_labels(new.d, d4a = "make life")
  temp.d <- data.frame (new.d, d4a)  
  
  result<-questionr::freq(temp.d$d4a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.")
a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 813 51.6 53.4 51.6 53.4
Somewhat_Agree 599 38.0 39.3 89.7 92.7
Somewhat_Disagree 94 6.0 6.2 95.6 98.9
Strongly_Disagree 17 1.1 1.1 96.7 100.0
NA 52 3.3 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
# b. Once you make up your mind to do something, you stay with it until the job is completely done.
  d4b <- as.factor(d[,"d4b"])
  # Make "*" to NA
d4b[which(d4b=="*")]<-"NA"
  levels(d4b) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4b <- ordered(d4b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4b)
  new.d <- apply_labels(new.d, d4b = "until job is done")
  temp.d <- data.frame (new.d, d4b)  
  
  result<-questionr::freq(temp.d$d4b,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Once you make up your mind to do something, you stay with it until the job is completely done.")
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 1027 65.2 67.3 65.2 67.3
Somewhat_Agree 457 29.0 29.9 94.2 97.2
Somewhat_Disagree 38 2.4 2.5 96.6 99.7
Strongly_Disagree 4 0.3 0.3 96.9 100.0
NA 49 3.1 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
# c. You like doing things that other people thought could not be done.
  d4c <- as.factor(d[,"d4c"])
  # Make "*" to NA
d4c[which(d4c=="*")]<-"NA"
  levels(d4c) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4c <- ordered(d4c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4c)
  new.d <- apply_labels(new.d, d4c = "until job is done")
  temp.d <- data.frame (new.d, d4c)  
  
  result<-questionr::freq(temp.d$d4c,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. You like doing things that other people thought could not be done.")
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 731 46.4 48.4 46.4 48.4
Somewhat_Agree 599 38.0 39.6 84.4 88.0
Somewhat_Disagree 152 9.7 10.1 94.1 98.1
Strongly_Disagree 29 1.8 1.9 95.9 100.0
NA 64 4.1 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
# d. When things don’t go the way you want them to, that just makes you work even harder.
  d4d <- as.factor(d[,"d4d"])
  # Make "*" to NA
d4d[which(d4d=="*")]<-"NA"
  levels(d4d) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4d <- ordered(d4d, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4d)
  new.d <- apply_labels(new.d, d4d = "until job is done")
  temp.d <- data.frame (new.d, d4d)  
  
  result<-questionr::freq(temp.d$d4d,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. When things don’t go the way you want them to, that just makes you work even harder.")
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 770 48.9 50.7 48.9 50.7
Somewhat_Agree 607 38.5 40.0 87.4 90.7
Somewhat_Disagree 120 7.6 7.9 95.0 98.6
Strongly_Disagree 22 1.4 1.4 96.4 100.0
NA 56 3.6 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
# e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
  d4e <- as.factor(d[,"d4e"])
  # Make "*" to NA
d4e[which(d4e=="*")]<-"NA"
  levels(d4e) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4e <- ordered(d4e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4e)
  new.d <- apply_labels(new.d, d4e = "do it yourself")
  temp.d <- data.frame (new.d, d4e)  
  
  result<-questionr::freq(temp.d$d4e,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.")
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 678 43.0 44.6 43.0 44.6
Somewhat_Agree 592 37.6 38.9 80.6 83.6
Somewhat_Disagree 202 12.8 13.3 93.5 96.8
Strongly_Disagree 48 3.0 3.2 96.5 100.0
NA 55 3.5 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
# f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
  d4f <- as.factor(d[,"d4f"])
  # Make "*" to NA
d4f[which(d4f=="*")]<-"NA"
  levels(d4f) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4f <- ordered(d4f, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4f)
  new.d <- apply_labels(new.d, d4f = "not easy but get it done")
  temp.d <- data.frame (new.d, d4f)  
  
  result<-questionr::freq(temp.d$d4f,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. It’s not always easy, but you manage to find a way to do the things you really need to get done.")
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 1014 64.4 66.4 64.4 66.4
Somewhat_Agree 472 30.0 30.9 94.3 97.3
Somewhat_Disagree 30 1.9 2.0 96.3 99.2
Strongly_Disagree 12 0.8 0.8 97.0 100.0
NA 47 3.0 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
# g. Very seldom have you been disappointed by the results of your hard work.
  d4g <- as.factor(d[,"d4g"])
  # Make "*" to NA
d4g[which(d4g=="*")]<-"NA"
  levels(d4g) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4g <- ordered(d4g, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4g)
  new.d <- apply_labels(new.d, d4g = "seldom disappointed")
  temp.d <- data.frame (new.d, d4g)  
  
  result<-questionr::freq(temp.d$d4g,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Very seldom have you been disappointed by the results of your hard work.")
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 540 34.3 35.7 34.3 35.7
Somewhat_Agree 736 46.7 48.6 81.0 84.3
Somewhat_Disagree 186 11.8 12.3 92.8 96.6
Strongly_Disagree 52 3.3 3.4 96.1 100.0
NA 61 3.9 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
# h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
  d4h <- as.factor(d[,"d4h"])
  # Make "*" to NA
d4h[which(d4h=="*")]<-"NA"
  levels(d4h) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4h <- ordered(d4h, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4h)
  new.d <- apply_labels(new.d, d4h = "stand up for believes")
  temp.d <- data.frame (new.d, d4h)  
  
  result<-questionr::freq(temp.d$d4h,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.")
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 972 61.7 63.8 61.7 63.8
Somewhat_Agree 484 30.7 31.8 92.4 95.5
Somewhat_Disagree 55 3.5 3.6 95.9 99.1
Strongly_Disagree 13 0.8 0.9 96.8 100.0
NA 51 3.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
# i. In the past, even when things got really tough, you never lost sight of your goals.
  d4i <- as.factor(d[,"d4i"])
    # Make "*" to NA
d4i[which(d4i=="*")]<-"NA"
  levels(d4i) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4i <- ordered(d4i, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4i)
  new.d <- apply_labels(new.d, d4i = "tough but never lost")
  temp.d <- data.frame (new.d, d4i)  
  
  result<-questionr::freq(temp.d$d4i,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. In the past, even when things got really tough, you never lost sight of your goals.")
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 889 56.4 58.3 56.4 58.3
Somewhat_Agree 537 34.1 35.2 90.5 93.6
Somewhat_Disagree 90 5.7 5.9 96.3 99.5
Strongly_Disagree 8 0.5 0.5 96.8 100.0
NA 51 3.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
#j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
  d4j <- as.factor(d[,"d4j"])
    # Make "*" to NA
d4j[which(d4j=="*")]<-"NA"
  levels(d4j) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4j <- ordered(d4j, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4j)
  new.d <- apply_labels(new.d, d4j = "the way you want to do matters")
  temp.d <- data.frame (new.d, d4j)  
  
  result<-questionr::freq(temp.d$d4j,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.")
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 528 33.5 34.7 33.5 34.7
Somewhat_Agree 635 40.3 41.7 73.8 76.4
Somewhat_Disagree 296 18.8 19.4 92.6 95.9
Strongly_Disagree 63 4.0 4.1 96.6 100.0
NA 53 3.4 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
#k. You don’t let your personal feelings get in the way of doing a job.
  d4k <- as.factor(d[,"d4k"])
    # Make "*" to NA
d4k[which(d4k=="*")]<-"NA"
  levels(d4k) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4k <- ordered(d4k, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4k)
  new.d <- apply_labels(new.d, d4k = "personal feelings never get in the way of job")
  temp.d <- data.frame (new.d, d4k)  
  
  result<-questionr::freq(temp.d$d4k,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. You don’t let your personal feelings get in the way of doing a job.")
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 830 52.7 54.5 52.7 54.5
Somewhat_Agree 573 36.4 37.6 89.1 92.1
Somewhat_Disagree 93 5.9 6.1 95.0 98.2
Strongly_Disagree 28 1.8 1.8 96.8 100.0
NA 51 3.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
#l. Hard work has really helped you to get ahead in life.
  d4l <- as.factor(d[,"d4l"])
    # Make "*" to NA
d4l[which(d4l=="*")]<-"NA"
  levels(d4l) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4l <- ordered(d4l, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4l)
  new.d <- apply_labels(new.d, d4l = "hard work helps")
  temp.d <- data.frame (new.d, d4l)  
  
  result<-questionr::freq(temp.d$d4l,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "l. Hard work has really helped you to get ahead in life.")
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 1056 67.0 69.0 67.0 69
Somewhat_Agree 397 25.2 25.9 92.3 95
Somewhat_Disagree 61 3.9 4.0 96.1 99
Strongly_Disagree 16 1.0 1.0 97.1 100
NA 45 2.9 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100

D5: Childhood

  • D5. The next questions are about the time period of your childhood, before the age of 18. These are standard questions asked in many surveys of life history. This information will allow us to understand how problems that may occur early in life may affect health later in life. This is a sensitive topic and some people may feel uncomfortable with these questions. Please keep in mind that you can skip any question you do not want to answer. All information is kept confidential. When you were growing up, during the first 18 years of your life…
    1. Did you live with anyone who was depressed, mentally ill, or suicidal?
    1. Did you live with anyone who was a problem drinker or alcoholic?
    1. Did you live with anyone who used illegal street drugs or who abused prescription medications?
    1. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
    1. Were your parents separated or divorced?
    1. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
    1. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way? Do not include spanking.
    1. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
    1. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
    1. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
    1. How often did anyone at least 5 years older than you or an adult, force you to have sex?
    • 1=No
    • 2=Yes
    • 3=Parents not married
    • 88=Don’t know/not sure
    • 99=Prefer not to answer”
# a. Did you live with anyone who was depressed, mentally ill, or suicidal?
  d5a <- as.factor(d[,"d5a"])
  # Make "*" to NA
d5a[which(d5a=="*")]<-"NA"
  levels(d5a) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5a <- ordered(d5a, c("No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5a)
  new.d <- apply_labels(new.d, d5a = "live with depressed")
  temp.d <- data.frame (new.d, d5a)  
  
  result<-questionr::freq(temp.d$d5a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you live with anyone who was depressed, mentally ill, or suicidal?")
a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 1318 83.7 85.8
Yes 105 6.7 6.8
Dont_know_not_sure 95 6.0 6.2
Prefer_not_to_answer 19 1.2 1.2
NA 38 2.4 NA
Total 1575 100.0 100.0 
# b. Did you live with anyone who was a problem drinker or alcoholic?
  d5b <- as.factor(d[,"d5b"])
# Make "*" to NA
d5b[which(d5b=="*")]<-"NA"
  levels(d5b) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5b <- ordered(d5b, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5b)
  new.d <- apply_labels(new.d, d5b = "live with alcoholic")
  temp.d <- data.frame (new.d, d5b)  
  
  result<-questionr::freq(temp.d$d5b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you live with anyone who was a problem drinker or alcoholic?")
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 1037 65.8 67.4
Yes 398 25.3 25.9
Dont_know_not_sure 71 4.5 4.6
Prefer_not_to_answer 33 2.1 2.1
NA 36 2.3 NA
Total 1575 100.0 100.0 
# c. Did you live with anyone who used illegal street drugs or who abused prescription medications?  
  d5c <- as.factor(d[,"d5c"])
# Make "*" to NA
d5c[which(d5c=="*")]<-"NA"
  levels(d5c) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5c <- ordered(d5c, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5c)
  new.d <- apply_labels(new.d, d5c = "live with illegal street drugs")
  temp.d <- data.frame (new.d, d5c)  
  
  result<-questionr::freq(temp.d$d5c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Did you live with anyone who used illegal street drugs or who abused prescription medications?")
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 1359 86.3 88.4
Yes 93 5.9 6.0
Dont_know_not_sure 65 4.1 4.2
Prefer_not_to_answer 21 1.3 1.4
NA 37 2.3 NA
Total 1575 100.0 100.0 
# d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility? 
  d5d <- as.factor(d[,"d5d"])
# Make "*" to NA
d5d[which(d5d=="*")]<-"NA"
  levels(d5d) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5d <- ordered(d5d, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5d)
  new.d <- apply_labels(new.d, d5d = "live with people in a prison")
  temp.d <- data.frame (new.d, d5d)  
  
  result<-questionr::freq(temp.d$d5d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?")
d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?
n % val%
No 1352 85.8 87.9
Yes 145 9.2 9.4
Dont_know_not_sure 21 1.3 1.4
Prefer_not_to_answer 20 1.3 1.3
NA 37 2.3 NA
Total 1575 100.0 100.0 
# e. Were your parents separated or divorced? 
  d5e <- as.factor(d[,"d5e"])
# Make "*" to NA
d5e[which(d5e=="*")]<-"NA"
  levels(d5e) <- list(No="1",
                     Yes="2",
                     Not_married="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5e <- ordered(d5e, c( "No","Yes","Not_married","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5e)
  new.d <- apply_labels(new.d, d5e = "parents divorced")
  temp.d <- data.frame (new.d, d5e)  
  
  result<-questionr::freq(temp.d$d5e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Were your parents separated or divorced?")
e. Were your parents separated or divorced?
n % val%
No 979 62.2 64.0
Yes 368 23.4 24.1
Not_married 128 8.1 8.4
Dont_know_not_sure 26 1.7 1.7
Prefer_not_to_answer 29 1.8 1.9
NA 45 2.9 NA
Total 1575 100.0 100.0 
# f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
  d5f <- as.factor(d[,"d5f"])
# Make "*" to NA
d5f[which(d5f=="*")]<-"NA"
  levels(d5f) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5f <- ordered(d5f, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5f)
  new.d <- apply_labels(new.d, d5f = "violence to each other")
  temp.d <- data.frame (new.d, d5f)  
  
  result<-questionr::freq(temp.d$d5f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?")
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 978 62.1 64.0
Once 87 5.5 5.7
More_than_once 210 13.3 13.8
Dont_know_not_sure 181 11.5 11.9
Prefer_not_to_answer 71 4.5 4.6
NA 48 3.0 NA
Total 1575 100.0 100.0 
#  g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
  d5g <- as.factor(d[,"d5g"])
# Make "*" to NA
d5g[which(d5g=="*")]<-"NA"
  levels(d5g) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5g <- ordered(d5g, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5g)
  new.d <- apply_labels(new.d, d5g = "violence to you")
  temp.d <- data.frame (new.d, d5g)  
  
  result<-questionr::freq(temp.d$d5g,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?")
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 1161 73.7 76.3
Once 49 3.1 3.2
More_than_once 200 12.7 13.1
Dont_know_not_sure 53 3.4 3.5
Prefer_not_to_answer 58 3.7 3.8
NA 54 3.4 NA
Total 1575 100.0 100.0 
# h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
  d5h <- as.factor(d[,"d5h"])
# Make "*" to NA
d5h[which(d5h=="*")]<-"NA"
  levels(d5h) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5h <- ordered(d5h, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5h)
  new.d <- apply_labels(new.d, d5h = "swear insult")
  temp.d <- data.frame (new.d, d5h)  
  
  result<-questionr::freq(temp.d$d5h,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?")
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 975 61.9 64.0
Once 59 3.7 3.9
More_than_once 312 19.8 20.5
Dont_know_not_sure 118 7.5 7.7
Prefer_not_to_answer 60 3.8 3.9
NA 51 3.2 NA
Total 1575 100.0 100.0 
# i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
  d5i <- as.factor(d[,"d5i"])
  # Make "*" to NA
d5i[which(d5i=="*")]<-"NA"
  levels(d5i) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5i <- ordered(d5i, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5i)
  new.d <- apply_labels(new.d, d5i = "touch you sexually")
  temp.d <- data.frame (new.d, d5i)  
  
  result<-questionr::freq(temp.d$d5i,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?")
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 1409 89.5 91.7
Once 52 3.3 3.4
More_than_once 38 2.4 2.5
Dont_know_not_sure 18 1.1 1.2
Prefer_not_to_answer 19 1.2 1.2
NA 39 2.5 NA
Total 1575 100.0 100.0 
# j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
  d5j <- as.factor(d[,"d5j"])
  # Make "*" to NA
d5j[which(d5j=="*")]<-"NA"
  levels(d5j) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5j <- ordered(d5j, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5j)
  new.d <- apply_labels(new.d, d5j = "touch them sexually")
  temp.d <- data.frame (new.d, d5j)  
  
  result<-questionr::freq(temp.d$d5j,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?")
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 1416 89.9 92.1
Once 49 3.1 3.2
More_than_once 32 2.0 2.1
Dont_know_not_sure 19 1.2 1.2
Prefer_not_to_answer 21 1.3 1.4
NA 38 2.4 NA
Total 1575 100.0 100.0 
# k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
  d5k <- as.factor(d[,"d5k"])
  # Make "*" to NA
d5k[which(d5k=="*")]<-"NA"
  levels(d5k) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5k <- ordered(d5k, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5k)
  new.d <- apply_labels(new.d, d5k = "forced to have sex")
  temp.d <- data.frame (new.d, d5k)  
  
  result<-questionr::freq(temp.d$d5k,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. How often did anyone at least 5 years older than you or an adult, force you to have sex?")
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 1465 93.0 95.3
Once 21 1.3 1.4
More_than_once 23 1.5 1.5
Dont_know_not_sure 9 0.6 0.6
Prefer_not_to_answer 19 1.2 1.2
NA 38 2.4 NA
Total 1575 100.0 100.0

E1: First indications

  • E1. What were the first indications that suggested that you might have prostate cancer (before you had a prostate biopsy)? Mark all that apply.
    • E1_1: 1=I had a high PSA (‘prostate specific antigen’) test
    • E1_2: 1=My doctor did a digital rectal exam that indicated an abnormality
    • E1_3: 1=I had urinary, sexual, or bowel problems that I went to see my doctor about
    • E1_4: 1=I had bone pain that I went to see my doctor about
    • E1_5: 1=I was fearful I had cancer
    • E1_6: 1=Other
# 1
  e1_1 <- as.factor(d[,"e1_1"])
  levels(e1_1) <- list(High_PSA_test="1")

  new.d <- data.frame(new.d, e1_1)
  new.d <- apply_labels(new.d, e1_1 = "High_PSA_test")
  temp.d <- data.frame (new.d, e1_1)  
  
  result<-questionr::freq(temp.d$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 1231 78.2 100
NA 344 21.8 NA
Total 1575 100.0 100 
#2
  e1_2 <- as.factor(d[,"e1_2"])
  levels(e1_2) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_2)
  new.d <- apply_labels(new.d, e1_2 = "digital rectal exam")
  temp.d <- data.frame (new.d, e1_2)  
  
  result<-questionr::freq(temp.d$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 437 27.7 100
NA 1138 72.3 NA
Total 1575 100.0 100 
#3
  e1_3 <- as.factor(d[,"e1_3"])
  e1_3[which(e1_3=="*")]<-"NA"
  levels(e1_3) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_3)
  new.d <- apply_labels(new.d, e1_3 = "urinary sexual or bowel problems")
  temp.d <- data.frame (new.d, e1_3)  
  
  result<-questionr::freq(temp.d$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 283 18 100
NA 1292 82 NA
Total 1575 100 100 
#4
  e1_4 <- as.factor(d[,"e1_4"])
  e1_4[which(e1_4=="*")]<-"NA"
  levels(e1_4) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_4)
  new.d <- apply_labels(new.d, e1_4 = "bone pain")
  temp.d <- data.frame (new.d, e1_4)  
  
  result<-questionr::freq(temp.d$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 25 1.6 100
NA 1550 98.4 NA
Total 1575 100.0 100 
#5
  e1_5 <- as.factor(d[,"e1_5"])
  e1_5[which(e1_5=="*")]<-"NA"
  levels(e1_5) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_5)
  new.d <- apply_labels(new.d, e1_5 = "fearful")
  temp.d <- data.frame (new.d, e1_5)  
  
  result<-questionr::freq(temp.d$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 65 4.1 100
NA 1510 95.9 NA
Total 1575 100.0 100

E1 Other: First indications

e1other <- d[,"e1other"]
e1other[which(e1other=="#NAME?")]<-"NA"

  new.d <- data.frame(new.d, e1other)
  new.d <- apply_labels(new.d, e1other = "e1other")
  temp.d <- data.frame (new.d, e1other)
result<-questionr::freq(temp.d$e1other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E1 Other")
E1 Other
n % val%
Agent Orange Vietnam 1972 1 0.1 1.4
Always weak. 1 0.1 1.4
As a Vietnam Veteran, I was assigned to an agent orange area. 1 0.1 1.4
Be check 1 0.1 1.4
Biopsy 1 0.1 1.4
Blood in my urine 1 0.1 1.4
Blood in urine 3 0.2 4.1
Blood in urine (from light pink to straight blood). 1 0.1 1.4
Blood in urine. 1 0.1 1.4
Brother previously diagnosed 1 0.1 1.4
Did not have a clue. 1 0.1 1.4
Didn’t know I had it 1 0.1 1.4
Difficulty urinating 1 0.1 1.4
Digital exam showed no abnormality in size of prostate 1 0.1 1.4
Doctors check up 1 0.1 1.4
Don’t remember 1 0.1 1.4
Enlarged prostate surgery 1991 1 0.1 1.4
Exposed to agent orange Vietnam June 1970 to May 1971 1 0.1 1.4
Family history 1 0.1 1.4
feeling having to urinate but wouldn’t actually go 1 0.1 1.4
Found during my yearly check up 1 0.1 1.4
Found during urinary problem, frequent urination surgery on prostate —- 1 0.1 1.4
Frequent urination 1 0.1 1.4
Had blood in bowel went to see doctor 1 0.1 1.4
Had fluctuating PSA 1 0.1 1.4
Had swelling in leg, blood clot. 1 0.1 1.4
Had trauma-stomach surgery, blood work Oct. 2014 1 0.1 1.4
Heavy pain in my right kidney 1 0.1 1.4
I also had an enlarged prostate. 1 0.1 1.4
I also had rectal exam 1 0.1 1.4
I had a tingling feeling in my scrotum. 1 0.1 1.4
I had enlarged prostate during one of my routine check ups, my doctor found out that my PSA was high. 1 0.1 1.4
I had urinary 1 0.1 1.4
I was admitted to the hospital because I was very sick that’s when they found out that I have cancer. 1 0.1 1.4
I was in so much pain 1 0.1 1.4
I went for my exam. 1 0.1 1.4
Kidney program tested my blood 1 0.1 1.4
Low level fever fatigue. 1 0.1 1.4
My body was reacting abnormally for a period of time. 1 0.1 1.4
My dog Jackson kept poking my groin with his nose there stare at me. 1 0.1 1.4
My father and grandfather died from cancer. 1 0.1 1.4
My PCP found blood in my urine test. 1 0.1 1.4
My PSA was being checked on my regular doctor visits. 1 0.1 1.4
My sex life is over, no more sex. 1 0.1 1.4
Never 1 0.1 1.4
None I just had pain 1 0.1 1.4
Normal urological blood work showed-PSA 1 0.1 1.4
On my double 18 wheeler truck accident, I had multiple internal injuries that is how in one of the test doctor said it looks like you may have —- 1 0.1 1.4
PET Scan 1 0.1 1.4
Physical 1 0.1 1.4
Physical and finger up posterior 1 0.1 1.4
Physical exam. 1 0.1 1.4
Problem urinating 1 0.1 1.4
Prostate swell 1 0.1 1.4
PSA levels kept rising. 1 0.1 1.4
Regular check up 1 0.1 1.4
Regular prostate check up felt a lump 1 0.1 1.4
Routine exam 1 0.1 1.4
Sharp pain in right lower region. 1 0.1 1.4
Something was discovered during routine health check up. 1 0.1 1.4
Starting having erection problems 1 0.1 1.4
Swollen and tender to touch 1 0.1 1.4
testing done 1 0.1 1.4
Urinating slow 1 0.1 1.4
Vietnam was Agent Orange 1 0.1 1.4
Visible blood in ejaculate (once). 1 0.1 1.4
Waking up thru the night using the bathroom often 1 0.1 1.4
Weakness and loss of weight. 1 0.1 1.4
Went to doctor for check up 1 0.1 1.4
When VA check me 1 0.1 1.4
With no actual symptoms 1 0.1 1.4
Yearly check-up. 1 0.1 1.4
NA 1501 95.3 NA
Total 1575 100.0 100.0

E2: Before diagnosis

  • E2. Before you were diagnosed with prostate cancer:
      1. Did you have any previous prostate biopsies that were negative?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3 or more
      1. Did you have any previous PSA blood tests that were considered normal?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3
      • 4=4
      • 5=5 or more
# 1
  e2aa <- as.factor(d[,"e2aa"])
# Make "*" to NA
e2aa[which(e2aa=="*")]<-"NA"
  levels(e2aa) <- list(Yes="2",
                      No="1",
                      Dont_know="88")
  e2aa <- ordered(e2aa, c("Yes","No","Dont_know"))
  
  new.d <- data.frame(new.d, e2aa)
  new.d <- apply_labels(new.d, e2aa = "prostate biopsies")
  temp.d <- data.frame (new.d, e2aa)  
  
  result<-questionr::freq(temp.d$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 211 13.4 14.0
No 1095 69.5 72.9
Dont_know 197 12.5 13.1
NA 72 4.6 NA
Total 1575 100.0 100.0 
#2
  e2ab <- as.factor(d[,"e2ab"])
# Make "*" to NA
e2ab[which(e2ab=="*")]<-"NA"
  levels(e2ab) <- list(One="1",
                      Two="2",
                      Three_more="3")
  e2ab <- ordered(e2ab, c("One","Two","Three_more"))
  
  new.d <- data.frame(new.d, e2ab)
  new.d <- apply_labels(new.d, e2ab = "prostate biopsies_How many")
  temp.d <- data.frame (new.d, e2ab)  
  
  result<-questionr::freq(temp.d$e2ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
n % val%
One 117 7.4 50.2
Two 61 3.9 26.2
Three_more 55 3.5 23.6
NA 1342 85.2 NA
Total 1575 100.0 100.0 
#3
  e2ba <- as.factor(d[,"e2ba"])
# Make "*" to NA
e2ba[which(e2ba=="*")]<-"NA"
  levels(e2ba) <- list(Yes="2",
                       No="1",
                       Dont_know="88")
  e2ba <- ordered(e2ba, c("Yes","No","Dont_know"))
  
  new.d <- data.frame(new.d, e2ba)
  new.d <- apply_labels(new.d, e2ba = "PSA blood tests")
  temp.d <- data.frame (new.d, e2ba)  
  
  result<-questionr::freq(temp.d$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 621 39.4 43.8
No 375 23.8 26.4
Dont_know 423 26.9 29.8
NA 156 9.9 NA
Total 1575 100.0 100.0 
#4
  e2bb <- as.factor(d[,"e2bb"])
  # Make "*" to NA
e2bb[which(e2bb=="*")]<-"NA"
  levels(e2bb) <- list(One="1",
                      Two="2",
                      Three="3",
                      Four="4",
                      Five_more="5")
  e2bb <- ordered(e2bb, c("One","Two","Threem","Four","Five_more"))
  
  new.d <- data.frame(new.d, e2bb)
  new.d <- apply_labels(new.d, e2bb = "PSA blood tests_how many")
  temp.d <- data.frame (new.d, e2bb)  
  
  result<-questionr::freq(temp.d$e2bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
n % val%
One 64 4.1 12.5
Two 118 7.5 23.0
Threem 0 0.0 0.0
Four 52 3.3 10.1
Five_more 279 17.7 54.4
NA 1062 67.4 NA
Total 1575 100.0 100.0

E3: Decision about PSA blood test

  • E3. Which of the following best describes your decision to have the PSA blood test that indicated that you had prostate cancer?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4= I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor, nurse, or health care provider made the decision
    • 88=I do not know or remember how the decision was made
  e3 <- as.factor(d[,"e3"])
# Make "*" to NA
e3[which(e3=="*")]<-"NA"
  levels(e3) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88")
  e3 <- ordered(e3, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e3)
  new.d <- apply_labels(new.d, e3 = "decision to have the PSA blood test")
  temp.d <- data.frame (new.d, e3)  
  
  result<-questionr::freq(temp.d$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 203 12.9 13.7
With_family_or_friends 107 6.8 7.2
With_family_and_doctor 219 13.9 14.8
With_doctor 386 24.5 26.1
Doctor_made 449 28.5 30.3
Dont_know_or_remember 116 7.4 7.8
NA 95 6.0 NA
Total 1575 100.0 100.0

E4: Understanding of aggressiveness

  • E4. When you were diagnosed with prostate cancer, what was your understanding of how aggressive your cancer might be (i.e., how likely it was that your cancer might progress).
    • 1=Low risk of progression
    • 2=Intermediate risk of progression
    • 3=High risk of progression
    • 4=Unknown risk of progression
    • 88=Don’t know/Don’t remember
  e4 <- as.factor(d[,"e4"])
# Make "*" to NA
e4[which(e4=="*")]<-"NA"
  levels(e4) <- list(Low_risk="1",
                     Intermediate_risk="2",
                     High_risk="3",
                     Unknown_risk="4",
                     Dont_know_or_remember="88")
  e4 <- ordered(e4, c("Low_risk","Intermediate_risk","High_risk","Unknown_risk","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e4)
  new.d <- apply_labels(new.d, e4 = "how aggressive")
  temp.d <- data.frame (new.d, e4)  
  
  result<-questionr::freq(temp.d$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 557 35.4 36.5
Intermediate_risk 244 15.5 16.0
High_risk 330 21.0 21.6
Unknown_risk 139 8.8 9.1
Dont_know_or_remember 255 16.2 16.7
NA 50 3.2 NA
Total 1575 100.0 100.0

E5: Gleason score

  • E5. What was your Gleason score when you were diagnosed with prostate cancer?
    • 1=6 or less
    • 2=7
    • 3=8-10
    • 88=Don’t know
  e5 <- as.factor(d[,"e5"])
# Make "*" to NA
e5[which(e5=="*")]<-"NA"
  levels(e5) <- list(Six_less="1",
                     Seven="2",
                     Eight_to_ten="3",
                     Dont_know="88")
  e5 <- ordered(e5, c("Six_less","Seven","Eight_to_ten","Dont_know"))
  
  new.d <- data.frame(new.d, e5)
  new.d <- apply_labels(new.d, e5 = "Gleason score")
  temp.d <- data.frame (new.d, e5)  
  
  result<-questionr::freq(temp.d$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 292 18.5 19.5
Seven 229 14.5 15.3
Eight_to_ten 223 14.2 14.9
Dont_know 755 47.9 50.4
NA 76 4.8 NA
Total 1575 100.0 100.0

E6: Understanding of stage

  • E6. What was your understanding of the stage of your prostate cancer when you were diagnosed?
    • 1=Localized, confined to prostate
    • 2=Regional, tumor extended to regions around the prostate
    • 3=Distant, tumor extended to bones or other parts of body
    • 88=Don’t know about the stage
  e6 <- as.factor(d[,"e6"])
# Make "*" to NA
e6[which(e6=="*")]<-"NA"
  levels(e6) <- list(Localized="1",
                     Regional="2",
                     Distant="3",
                     Dont_know="88")
  e6 <- ordered(e6, c("Localized","Regional","Distant","Dont_know"))
  
  new.d <- data.frame(new.d, e6)
  new.d <- apply_labels(new.d, e6 = "Stage")
  temp.d <- data.frame (new.d, e6)  
  
  result<-questionr::freq(temp.d$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 1018 64.6 67.4
Regional 66 4.2 4.4
Distant 27 1.7 1.8
Dont_know 400 25.4 26.5
NA 64 4.1 NA
Total 1575 100.0 100.0

E7: MRI guided biopsy

  • E7. Did you have a Magnetic Resonance Imaging (MRI)-guided biopsy to diagnose your cancer? (This is a different type of biopsy than the standard ultrasound biopsy that involves taking 12 random biopsy core samples. Instead, you would be placed in a large donut shaped machine that can be noisy. With assistance from the MRI, 2-3 targeted biopsies would be taken in areas of the tumor shown to be most aggressive.)
    • 2=Yes
    • 1=No
    • 88=Don’t Know
  e7 <- as.factor(d[,"e7"])
# Make "*" to NA
e7[which(e7=="*")]<-"NA"
  levels(e7) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e7 <- ordered(e7, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e7)
  new.d <- apply_labels(new.d, e7 = "Stage")
  temp.d <- data.frame (new.d, e7)  
  
  result<-questionr::freq(temp.d$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 508 32.3 33.8
Yes 551 35.0 36.7
Dont_know 443 28.1 29.5
NA 73 4.6 NA
Total 1575 100.0 100.0

E8: Decision about treatment

  • E8. How did you make your treatment decision?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4=I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor , nurse, or health care provider made the decision
    • 6=I don’t know or remember how the decision was made
  e8 <- as.factor(d[,"e8"])
# Make "*" to NA
e8[which(e8=="*")]<-"NA"
  levels(e8) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88")
  e8 <- ordered(e8, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e8)
  new.d <- apply_labels(new.d, e8 = "treatment decision")
  temp.d <- data.frame (new.d, e8)  
  
  result<-questionr::freq(temp.d$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 187 11.9 12.7
With_family_or_friends 229 14.5 15.6
With_family_and_doctor 575 36.5 39.1
With_doctor 341 21.7 23.2
Doctor_made 139 8.8 9.4
Dont_know_or_remember 0 0.0 0.0
NA 104 6.6 NA
Total 1575 100.0 100.0

E9: The most important factors of tx

  • E9. What were the most important factors you considered in making your treatment decision? Mark all that apply.
    • E9_1: 1=Best chance for cure of my cancer
    • E9_2: 1=Minimize side effects related to sexual function
    • E9_3: 1=Minimize side effects related to urinary function
    • E9_4: 1=Minimize side effects related to bowel function
    • E9_5: 1=Minimize financial cost
    • E9_6: 1=Amount of time and travel required to receive treatments
    • E9_7: 1=Length of recovery time
    • E9_8: 1=Amount of time away from work
    • E9_9: 1=Burden on family members
    • E9_10: 1=Reduce worry and concern about cancer
  e9_1 <- as.factor(d[,"e9_1"])
  levels(e9_1) <- list(Best_for_cure="1")
  new.d <- data.frame(new.d, e9_1)
  new.d <- apply_labels(new.d, e9_1 = "Best for cure")
  temp.d <- data.frame (new.d, e9_1)  
  result<-questionr::freq(temp.d$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 1348 85.6 100
NA 227 14.4 NA
Total 1575 100.0 100 
  e9_2 <- as.factor(d[,"e9_2"])
  levels(e9_2) <- list(side_effects_sexual="1")
  new.d <- data.frame(new.d, e9_2)
  new.d <- apply_labels(new.d, e9_2 = "side effects sexual")
  temp.d <- data.frame (new.d, e9_2)  
  result<-questionr::freq(temp.d$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 438 27.8 100
NA 1137 72.2 NA
Total 1575 100.0 100 
  e9_3 <- as.factor(d[,"e9_3"])
  levels(e9_3) <- list(side_effects_urinary="1")
  new.d <- data.frame(new.d, e9_3)
  new.d <- apply_labels(new.d, e9_3 = "side effects urinary")
  temp.d <- data.frame (new.d, e9_3)  
  result<-questionr::freq(temp.d$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 388 24.6 100
NA 1187 75.4 NA
Total 1575 100.0 100 
  e9_4 <- as.factor(d[,"e9_4"])
  levels(e9_4) <- list(side_effects_bowel="1")
  new.d <- data.frame(new.d, e9_4)
  new.d <- apply_labels(new.d, e9_4 = "side effects bowel")
  temp.d <- data.frame (new.d, e9_4)  
  result<-questionr::freq(temp.d$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 185 11.7 100
NA 1390 88.3 NA
Total 1575 100.0 100 
  e9_5 <- as.factor(d[,"e9_5"])
  levels(e9_5) <- list(financial_cost="1")
  new.d <- data.frame(new.d, e9_5)
  new.d <- apply_labels(new.d, e9_5 = "financial cost")
  temp.d <- data.frame (new.d, e9_5)  
  result<-questionr::freq(temp.d$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 94 6 100
NA 1481 94 NA
Total 1575 100 100 
  e9_6 <- as.factor(d[,"e9_6"])
  levels(e9_6) <- list(time_and_travel="1")
  new.d <- data.frame(new.d, e9_6)
  new.d <- apply_labels(new.d, e9_6 = "time and travel")
  temp.d <- data.frame (new.d, e9_6)  
  result<-questionr::freq(temp.d$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 180 11.4 100
NA 1395 88.6 NA
Total 1575 100.0 100 
  e9_7 <- as.factor(d[,"e9_7"])
  levels(e9_7) <- list(recovery_time="1")
  new.d <- data.frame(new.d, e9_7)
  new.d <- apply_labels(new.d, e9_7 = "recovery time")
  temp.d <- data.frame (new.d, e9_7)  
  result<-questionr::freq(temp.d$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 293 18.6 100
NA 1282 81.4 NA
Total 1575 100.0 100 
  e9_8 <- as.factor(d[,"e9_8"])
  levels(e9_8) <- list(time_away_from_work="1")
  new.d <- data.frame(new.d, e9_8)
  new.d <- apply_labels(new.d, e9_8 = "time away from work")
  temp.d <- data.frame (new.d, e9_8)  
  result<-questionr::freq(temp.d$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 112 7.1 100
NA 1463 92.9 NA
Total 1575 100.0 100 
  e9_9 <- as.factor(d[,"e9_9"])
  levels(e9_9) <- list(family_burden="1")
  new.d <- data.frame(new.d, e9_9)
  new.d <- apply_labels(new.d, e9_9 = "family burden")
  temp.d <- data.frame (new.d, e9_9)  
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 227 14.4 100
NA 1348 85.6 NA
Total 1575 100.0 100 
  e9_10 <- as.factor(d[,"e9_10"])
  levels(e9_10) <- list(Reduce_worry_concern="1")
  new.d <- data.frame(new.d, e9_10)
  new.d <- apply_labels(new.d, e9_10 = "Reduce worry and concern")
  temp.d <- data.frame (new.d, e9_10)  
  result<-questionr::freq(temp.d$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 622 39.5 100
NA 953 60.5 NA
Total 1575 100.0 100

E10: Recieved treatment

  • E10. Please mark all the treatments that you have received for your prostate cancer? Mark all that apply.
    • E10_1: 1=Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
    • E10_2: 1=Active Surveillance or watchful waiting
    • E10_3: 1=Prostate surgery (prostatectomy)
    • E10_4: 1=Radiation to the prostate
    • E10_5: 1=Hormonal treatments
    • E10_6: 1=Provenge/immunotherapy (Sipuleucel T)
    • E10_7: 1=Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
    • E10_8: 1=Other treatments to the prostate (HIFU (High Intensity Focused Ultrasound), RFA (Radio Frequency Ablation), laser, focal therapy, cryotherapy (freezing of the prostate))
  e10_1 <- as.factor(d[,"e10_1"])
  levels(e10_1) <- list(no_treatment="1")
  new.d <- data.frame(new.d, e10_1)
  new.d <- apply_labels(new.d, e10_1 = "no treatment")
  temp.d <- data.frame (new.d, e10_1)  
  result<-questionr::freq(temp.d$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 84 5.3 100
NA 1491 94.7 NA
Total 1575 100.0 100 
  e10_2 <- as.factor(d[,"e10_2"])
  levels(e10_2) <- list(Active_Surveillance="1")
  new.d <- data.frame(new.d, e10_2)
  new.d <- apply_labels(new.d, e10_2 = "Active Surveillance")
  temp.d <- data.frame (new.d, e10_2)  
  result<-questionr::freq(temp.d$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 220 14 100
NA 1355 86 NA
Total 1575 100 100 
  e10_3 <- as.factor(d[,"e10_3"])
  levels(e10_3) <- list(prostatectomy="1")
  new.d <- data.frame(new.d, e10_3)
  new.d <- apply_labels(new.d, e10_3 = "prostatectomy")
  temp.d <- data.frame (new.d, e10_3)  
  result<-questionr::freq(temp.d$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 372 23.6 100
NA 1203 76.4 NA
Total 1575 100.0 100 
  e10_4 <- as.factor(d[,"e10_4"])
  levels(e10_4) <- list(Radiation="1")
  new.d <- data.frame(new.d, e10_4)
  new.d <- apply_labels(new.d, e10_4 = "Radiation")
  temp.d <- data.frame (new.d, e10_4)  
  result<-questionr::freq(temp.d$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 617 39.2 100
NA 958 60.8 NA
Total 1575 100.0 100 
  e10_5 <- as.factor(d[,"e10_5"])
  levels(e10_5) <- list(Hormonal_treatments="1")
  new.d <- data.frame(new.d, e10_5)
  new.d <- apply_labels(new.d, e10_5 = "Hormonal treatments")
  temp.d <- data.frame (new.d, e10_5)  
  result<-questionr::freq(temp.d$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 161 10.2 100
NA 1414 89.8 NA
Total 1575 100.0 100 
  e10_6 <- as.factor(d[,"e10_6"])
  levels(e10_6) <- list(Provenge_immunotherapy="1")
  new.d <- data.frame(new.d, e10_6)
  new.d <- apply_labels(new.d, e10_6 = "Provenge immunotherapy")
  temp.d <- data.frame (new.d, e10_6)  
  result<-questionr::freq(temp.d$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 18 1.1 100
NA 1557 98.9 NA
Total 1575 100.0 100 
  e10_7 <- as.factor(d[,"e10_7"])
  levels(e10_7) <- list(Chemotherapy="1")
  new.d <- data.frame(new.d, e10_7)
  new.d <- apply_labels(new.d, e10_7 = "Chemotherapy")
  temp.d <- data.frame (new.d, e10_7)  
  result<-questionr::freq(temp.d$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 48 3 100
NA 1527 97 NA
Total 1575 100 100 
  e10_8 <- as.factor(d[,"e10_8"])
  levels(e10_8) <- list(Other="1")
  new.d <- data.frame(new.d, e10_8)
  new.d <- apply_labels(new.d, e10_8 = "Other")
  temp.d <- data.frame (new.d, e10_8)  
  result<-questionr::freq(temp.d$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 96 6.1 100
NA 1479 93.9 NA
Total 1575 100.0 100

E10-3 Prostatectomy

  • E10_3. Prostate surgery (prostatectomy), indicate which type(s):
    • E10_3_1: 1=Robotic or laproscopic surgery resulting in removal of the prostate
    • E10_3_2: 1=Open surgical removal of the prostate (using a long incision)
    • E10_3_3: 1=Had surgery but unsure of type
  e10_3_1 <- as.factor(d[,"e10_3_1"])
  levels(e10_3_1) <- list(Robotic_laproscopic_surgery="1")
  new.d <- data.frame(new.d, e10_3_1)
  new.d <- apply_labels(new.d, e10_3_1 = "Robotic or laproscopic surgery")
  temp.d <- data.frame (new.d, e10_3_1)  
  result<-questionr::freq(temp.d$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 420 26.7 100
NA 1155 73.3 NA
Total 1575 100.0 100 
  e10_3_2 <- as.factor(d[,"e10_3_2"])
  levels(e10_3_2) <- list(Open_surgical_removal="1")
  new.d <- data.frame(new.d, e10_3_2)
  new.d <- apply_labels(new.d, e10_3_2 = "Open surgical removal")
  temp.d <- data.frame (new.d, e10_3_2)  
  result<-questionr::freq(temp.d$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 80 5.1 100
NA 1495 94.9 NA
Total 1575 100.0 100 
  e10_3_3 <- as.factor(d[,"e10_3_3"])
  levels(e10_3_3) <- list(unsure_of_type="1")
  new.d <- data.frame(new.d, e10_3_3)
  new.d <- apply_labels(new.d, e10_3_3 = "unsure of type")
  temp.d <- data.frame (new.d, e10_3_3)  
  result<-questionr::freq(temp.d$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 129 8.2 100
NA 1446 91.8 NA
Total 1575 100.0 100

E10-4 Radiation

  • E10_4. Radiation to the prostate, indicate which type(s):
    • E10_4_1: 1=External beam radiation, where beams are aimed from the outside of your body (including IMRT (Intensity Modulated Radiation Therapy), IGRT (Image-Guided Radiation Therapy), arc therapy, proton beam, cyberknife, or 3D-conformal beam therapy)
    • E10_4_2: 1 = Insertion of radiation seed/roods (brachytherapy)
    • E10_4_3: 1=Other types of radiation therapy, or unsure of what type
  e10_4_1 <- as.factor(d[,"e10_4_1"])
  levels(e10_4_1) <- list(External_beam_radiation="1")
  new.d <- data.frame(new.d, e10_4_1)
  new.d <- apply_labels(new.d, e10_4_1 = "External beam radiation")
  temp.d <- data.frame (new.d, e10_4_1)  
  result<-questionr::freq(temp.d$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 609 38.7 100
NA 966 61.3 NA
Total 1575 100.0 100 
  e10_4_2 <- as.factor(d[,"e10_4_2"])
  levels(e10_4_2) <- list(brachytherapy="1")
  new.d <- data.frame(new.d, e10_4_2)
  new.d <- apply_labels(new.d, e10_4_2 = "brachytherapy")
  temp.d <- data.frame (new.d, e10_4_2)  
  result<-questionr::freq(temp.d$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 396 25.1 100
NA 1179 74.9 NA
Total 1575 100.0 100 
  e10_4_3 <- as.factor(d[,"e10_4_3"])
  levels(e10_4_3) <- list(Other_types="1")
  new.d <- data.frame(new.d, e10_4_3)
  new.d <- apply_labels(new.d, e10_4_3 = "Other types")
  temp.d <- data.frame (new.d, e10_4_3)  
  result<-questionr::freq(temp.d$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 109 6.9 100
NA 1466 93.1 NA
Total 1575 100.0 100

E10-5 Hormonal treatments

  • E10_5. Hormonal treatments, indicate which type(s):
    • E10_5_1: 1=Hormone shots (Lupron, Zoladex, Firmagon, Eligard, Vantas)
    • E10_5_2: 1= Surgical removal of testicles (orchiectomy)
    • E10_5_3: 1=Casodex (bicalutamide) or Eulexin (flutamide) pills
    • E10_5_4: 1=Zytiga (abiraterone) or Xtandi (enzalutamide) pills
    • E10_5_5: 1=Had hormone treatment, but unsure of type
  e10_5_1 <- as.factor(d[,"e10_5_1"])
  levels(e10_5_1) <- list(Hormone_shots="1")
  new.d <- data.frame(new.d, e10_5_1)
  new.d <- apply_labels(new.d, e10_5_1 = "Hormone shots")
  temp.d <- data.frame (new.d, e10_5_1)  
  result<-questionr::freq(temp.d$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 263 16.7 100
NA 1312 83.3 NA
Total 1575 100.0 100 
  e10_5_2 <- as.factor(d[,"e10_5_2"])
  levels(e10_5_2) <- list(orchiectomy="1")
  new.d <- data.frame(new.d, e10_5_2)
  new.d <- apply_labels(new.d, e10_5_2 = "orchiectomy")
  temp.d <- data.frame (new.d, e10_5_2)  
  result<-questionr::freq(temp.d$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 26 1.7 100
NA 1549 98.3 NA
Total 1575 100.0 100 
  e10_5_3 <- as.factor(d[,"e10_5_3"])
  levels(e10_5_3) <- list(Casodex_Eulexin="1")
  new.d <- data.frame(new.d, e10_5_3)
  new.d <- apply_labels(new.d, e10_5_3 = "Casodex or Eulexin pills")
  temp.d <- data.frame (new.d, e10_5_3)  
  result<-questionr::freq(temp.d$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 35 2.2 100
NA 1540 97.8 NA
Total 1575 100.0 100 
  e10_5_4 <- as.factor(d[,"e10_5_4"])
  levels(e10_5_4) <- list(Zytiga_Xtandi="1")
  new.d <- data.frame(new.d, e10_5_4)
  new.d <- apply_labels(new.d, e10_5_4 = "Zytiga or Xtandi pills")
  temp.d <- data.frame (new.d, e10_5_4)  
  result<-questionr::freq(temp.d$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 28 1.8 100
NA 1547 98.2 NA
Total 1575 100.0 100 
  e10_5_5 <- as.factor(d[,"e10_5_5"])
  levels(e10_5_5) <- list(unsure_type="1")
  new.d <- data.frame(new.d, e10_5_5)
  new.d <- apply_labels(new.d, e10_5_5 = "unsure of type")
  temp.d <- data.frame (new.d, e10_5_5)  
  result<-questionr::freq(temp.d$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 95 6 100
NA 1480 94 NA
Total 1575 100 100

E11: Treatment decision

  • E11. Your treatment decision: How true is each of the following statements for you?
      1. I had all the information I needed when a treatment was chosen for my prostate cancer
      1. My doctors told me the whole story about the effects of treatment
      1. I knew the right questions to ask my doctor
      1. I had enough time to make a decision about my treatment
      1. I am satisfied with the choices I made in treating my prostate cancer
      1. I would recommend the treatment I had to a close relative or friend
      • 1=Not at all
      • 2=A little bit
      • 3=Somewhat
      • 4=Quite a bit
      • 5=Very much
  e11a <- as.factor(d[,"e11a"])
# Make "*" to NA
e11a[which(e11a=="*")]<-"NA"
  levels(e11a) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11a)
  new.d <- apply_labels(new.d, e11a = "all info")
  temp.d <- data.frame (new.d, e11a)  
  result<-questionr::freq(temp.d$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 52 3.3 3.5
A_little_bit 43 2.7 2.9
Somewhat 173 11.0 11.7
Quite_a_bit 390 24.8 26.3
Very_much 825 52.4 55.6
NA 92 5.8 NA
Total 1575 100.0 100.0 
  e11b <- as.factor(d[,"e11b"])
# Make "*" to NA
e11b[which(e11b=="*")]<-"NA"
  levels(e11b) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11b)
  new.d <- apply_labels(new.d, e11b = "be told about effects")
  temp.d <- data.frame (new.d, e11b)  
  result<-questionr::freq(temp.d$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 41 2.6 2.8
A_little_bit 44 2.8 3.0
Somewhat 176 11.2 11.8
Quite_a_bit 357 22.7 24.0
Very_much 870 55.2 58.5
NA 87 5.5 NA
Total 1575 100.0 100.0 
  e11c <- as.factor(d[,"e11c"])
  # Make "*" to NA
e11c[which(e11c=="*")]<-"NA"
  levels(e11c) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11c)
  new.d <- apply_labels(new.d, e11c = "right questions to ask")
  temp.d <- data.frame (new.d, e11c)  
  result<-questionr::freq(temp.d$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 130 8.3 8.8
A_little_bit 169 10.7 11.4
Somewhat 524 33.3 35.4
Quite_a_bit 290 18.4 19.6
Very_much 369 23.4 24.9
NA 93 5.9 NA
Total 1575 100.0 100.0 
  e11d <- as.factor(d[,"e11d"])
  # Make "*" to NA
e11d[which(e11d=="*")]<-"NA"
  levels(e11d) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11d)
  new.d <- apply_labels(new.d, e11d = "enough time to decide")
  temp.d <- data.frame (new.d, e11d)  
  result<-questionr::freq(temp.d$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 39 2.5 2.6
A_little_bit 58 3.7 3.9
Somewhat 238 15.1 16.0
Quite_a_bit 359 22.8 24.2
Very_much 791 50.2 53.3
NA 90 5.7 NA
Total 1575 100.0 100.0 
  e11e <- as.factor(d[,"e11e"])
  # Make "*" to NA
e11e[which(e11e=="*")]<-"NA"
  levels(e11e) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11e)
  new.d <- apply_labels(new.d, e11e = "satisfied with the choices")
  temp.d <- data.frame (new.d, e11e)  
  result<-questionr::freq(temp.d$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 64 4.1 4.3
A_little_bit 53 3.4 3.6
Somewhat 174 11.0 11.7
Quite_a_bit 205 13.0 13.8
Very_much 991 62.9 66.6
NA 88 5.6 NA
Total 1575 100.0 100.0 
  e11f <- as.factor(d[,"e11f"])
  # Make "*" to NA
e11f[which(e11f=="*")]<-"NA"
  levels(e11f) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11f)
  new.d <- apply_labels(new.d, e11f = "would recommend")
  temp.d <- data.frame (new.d, e11f)  
  result<-questionr::freq(temp.d$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 90 5.7 6.1
A_little_bit 53 3.4 3.6
Somewhat 202 12.8 13.7
Quite_a_bit 184 11.7 12.4
Very_much 949 60.3 64.2
NA 97 6.2 NA
Total 1575 100.0 100.0

E12: Instructions from doctors or nurses

  • E12. Have you ever received instructions from a doctor, nurse, or other health professional about who you should see for routine prostate cancer checkups or monitoring?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e12 <- as.factor(d[,"e12"])
# Make "*" to NA
e12[which(e12=="*")]<-"NA"
  levels(e12) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e12 <- ordered(e12, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e12)
  new.d <- apply_labels(new.d, e12 = "received instructions")
  temp.d <- data.frame (new.d, e12)  
  
  result<-questionr::freq(temp.d$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 180 11.4 12.0
Yes 1225 77.8 81.6
Dont_know 97 6.2 6.5
NA 73 4.6 NA
Total 1575 100.0 100.0

E13: # of PSA blood test

  • E13. Since your prostate cancer diagnosis, how many times have you had a PSA blood test?
    • 0=None
    • 1=1
    • 2=2
    • 3=3
    • 4=4 or more
    • 88=Don’t know/not sure
  e13 <- as.factor(d[,"e13"])
# Make "*" to NA
e13[which(e13=="*")]<-"NA"
  levels(e13) <- list(None="0",
                      One="1",
                      Two="2",
                     Three="3",
                     Four_more="4",
                     Dont_know="88")
  e13 <- ordered(e13, c("None","One","Two","Three","Four_more","Dont_know"))
  
  new.d <- data.frame(new.d, e13)
  new.d <- apply_labels(new.d, e13 = "times of PSA blood test")
  temp.d <- data.frame (new.d, e13)  
  
  result<-questionr::freq(temp.d$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 36 2.3 2.4
One 31 2.0 2.1
Two 83 5.3 5.5
Three 145 9.2 9.6
Four_more 1052 66.8 69.7
Dont_know 163 10.3 10.8
NA 65 4.1 NA
Total 1575 100.0 100.0

E14: Be told PSA was rising

  • E14. Since diagnosis or treatment, have you ever been told that your PSA was rising?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e14 <- as.factor(d[,"e14"])
# Make "*" to NA
e14[which(e14=="*")]<-"NA"
  levels(e14) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e14 <- ordered(e14, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e14)
  new.d <- apply_labels(new.d, e14 = "been told PSA was rising")
  temp.d <- data.frame (new.d, e14)  
  
  result<-questionr::freq(temp.d$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 1079 68.5 72.0
Yes 309 19.6 20.6
Dont_know 111 7.0 7.4
NA 76 4.8 NA
Total 1575 100.0 100.0

E15: Recurred or got worse

  • E15. Since you were diagnosed, did your doctor ever tell you that your prostate cancer came back (recurred) or progressed (got worse)?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e15 <- as.factor(d[,"e15"])
# Make "*" to NA
e15[which(e15=="*")]<-"NA"
  levels(e15) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e15 <- ordered(e15, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e15)
  new.d <- apply_labels(new.d, e15 = "been told recurred progressed")
  temp.d <- data.frame (new.d, e15)  
  
  result<-questionr::freq(temp.d$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 1292 82.0 86.0
Yes 132 8.4 8.8
Dont_know 78 5.0 5.2
NA 73 4.6 NA
Total 1575 100.0 100.0

F1: Height

  • F1. How tall are you?
  f1cm <- d[,"f1cm"]
 
  new.d <- data.frame(new.d, f1cm)
  new.d <- apply_labels(new.d, f1cm = "height in cm")
  temp.d <- data.frame (new.d, f1cm)  
  
  result<-questionr::freq(temp.d$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How tall are you? (cm)")
How tall are you? (cm)
n % val%
0 6 0.4 10.3
0 0 1 0.1 1.7
0 9 1 0.1 1.7
1 8 0.5 13.8
10 3 0.2 5.2
11 3 0.2 5.2
12 1 0.1 1.7
135 1 0.1 1.7
151 1 0.1 1.7
170 1 0.1 1.7
173 1 0.1 1.7
175 1 0.1 1.7
178 1 0.1 1.7
180 1 0.1 1.7
181 1 0.1 1.7
190 3 0.2 5.2
2 3 0.2 5.2
200 1 0.1 1.7
228 1 0.1 1.7
234 1 0.1 1.7
245 1 0.1 1.7
247 1 0.1 1.7
265 1 0.1 1.7
280 1 0.1 1.7
44 1 0.1 1.7
47 1 0.1 1.7
5 2 0.1 3.4
6 2 0.1 3.4
7 2 0.1 3.4
72 1 0.1 1.7
8 1 0.1 1.7
9 4 0.3 6.9
NA 1517 96.3 NA
Total 1575 100.0 100.0

F2: Weight

  • F2. How much do you current weight?
  f2lbs <- d[,"f2lbs"]
  new.d <- data.frame(new.d, f2lbs)
  new.d <- apply_labels(new.d, f2lbs = "weight in lbs")
  temp.d <- data.frame (new.d, f2lbs)  
  result<-questionr::freq(temp.d$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (lbs)")
How much do you current weight? (lbs)
n % val%
* 2 0.1 0.2
* 3 1 0.1 0.1
* 4 1 0.1 0.1
* 5 1 0.1 0.1
* 9 1 0.1 0.1
*35 1 0.1 0.1
*4 1 0.1 0.1
*6 1 0.1 0.1
0 2 0.1 0.2
1 3 0.2 0.2
1 * 3 0.2 0.2
1 8 1 0.1 0.1
1* 1 0.1 0.1
100 1 0.1 0.1
105 1 0.1 0.1
106 1 0.1 0.1
110 2 0.1 0.2
120 2 0.1 0.2
121 1 0.1 0.1
122 1 0.1 0.1
125 3 0.2 0.2
130 4 0.3 0.3
132 1 0.1 0.1
134 1 0.1 0.1
135 4 0.3 0.3
137 1 0.1 0.1
139 1 0.1 0.1
14* 1 0.1 0.1
140 7 0.4 0.5
141 1 0.1 0.1
142 4 0.3 0.3
143 1 0.1 0.1
144 1 0.1 0.1
145 6 0.4 0.5
146 2 0.1 0.2
147 3 0.2 0.2
148 5 0.3 0.4
149 1 0.1 0.1
150 17 1.1 1.3
152 3 0.2 0.2
153 2 0.1 0.2
154 7 0.4 0.5
155 16 1.0 1.2
156 5 0.3 0.4
158 7 0.4 0.5
159 3 0.2 0.2
160 25 1.6 1.9
161 3 0.2 0.2
162 10 0.6 0.8
163 5 0.3 0.4
164 3 0.2 0.2
165 19 1.2 1.4
166 2 0.1 0.2
167 4 0.3 0.3
168 12 0.8 0.9
169 5 0.3 0.4
170 31 2.0 2.3
171 5 0.3 0.4
172 12 0.8 0.9
173 9 0.6 0.7
174 3 0.2 0.2
175 32 2.0 2.4
176 9 0.6 0.7
177 4 0.3 0.3
178 12 0.8 0.9
179 4 0.3 0.3
18 2 0.1 0.2
180 29 1.8 2.2
181 8 0.5 0.6
182 15 1.0 1.1
183 6 0.4 0.5
184 6 0.4 0.5
185 28 1.8 2.1
186 5 0.3 0.4
187 8 0.5 0.6
188 13 0.8 1.0
189 10 0.6 0.8
190 44 2.8 3.3
191 4 0.3 0.3
192 13 0.8 1.0
193 7 0.4 0.5
194 5 0.3 0.4
195 23 1.5 1.7
196 7 0.4 0.5
197 13 0.8 1.0
198 16 1.0 1.2
199 6 0.4 0.5
2 7 0.4 0.5
2 1 1 0.1 0.1
2 6 1 0.1 0.1
2* 3 0.2 0.2
200 46 2.9 3.5
201 2 0.1 0.2
202 9 0.6 0.7
203 6 0.4 0.5
204 8 0.5 0.6
205 28 1.8 2.1
206 2 0.1 0.2
207 5 0.3 0.4
208 8 0.5 0.6
209 8 0.5 0.6
210 44 2.8 3.3
211 3 0.2 0.2
212 10 0.6 0.8
213 4 0.3 0.3
214 9 0.6 0.7
215 39 2.5 2.9
216 3 0.2 0.2
217 7 0.4 0.5
218 12 0.8 0.9
219 5 0.3 0.4
220 38 2.4 2.9
221 5 0.3 0.4
222 8 0.5 0.6
223 9 0.6 0.7
224 4 0.3 0.3
225 19 1.2 1.4
226 2 0.1 0.2
227 5 0.3 0.4
228 7 0.4 0.5
229 5 0.3 0.4
230 24 1.5 1.8
231 2 0.1 0.2
232 6 0.4 0.5
233 4 0.3 0.3
234 4 0.3 0.3
235 23 1.5 1.7
236 5 0.3 0.4
237 3 0.2 0.2
238 5 0.3 0.4
239 1 0.1 0.1
240 23 1.5 1.7
241 1 0.1 0.1
242 9 0.6 0.7
243 3 0.2 0.2
244 3 0.2 0.2
245 22 1.4 1.7
246 4 0.3 0.3
247 6 0.4 0.5
248 5 0.3 0.4
249 3 0.2 0.2
250 25 1.6 1.9
251 1 0.1 0.1
252 3 0.2 0.2
253 2 0.1 0.2
254 3 0.2 0.2
255 8 0.5 0.6
256 1 0.1 0.1
257 2 0.1 0.2
258 2 0.1 0.2
259 2 0.1 0.2
260 13 0.8 1.0
261 1 0.1 0.1
262 2 0.1 0.2
263 1 0.1 0.1
264 2 0.1 0.2
265 12 0.8 0.9
266 2 0.1 0.2
267 3 0.2 0.2
270 11 0.7 0.8
271 1 0.1 0.1
272 3 0.2 0.2
274 1 0.1 0.1
276 2 0.1 0.2
277 1 0.1 0.1
278 3 0.2 0.2
279 3 0.2 0.2
280 12 0.8 0.9
282 1 0.1 0.1
284 2 0.1 0.2
285 5 0.3 0.4
286 1 0.1 0.1
287 1 0.1 0.1
288 1 0.1 0.1
289 2 0.1 0.2
29 1 0.1 0.1
290 2 0.1 0.2
292 1 0.1 0.1
295 5 0.3 0.4
297 2 0.1 0.2
298 3 0.2 0.2
3 1 0.1 0.1
300 5 0.3 0.4
302 1 0.1 0.1
305 1 0.1 0.1
306 1 0.1 0.1
309 1 0.1 0.1
310 4 0.3 0.3
315 2 0.1 0.2
317 1 0.1 0.1
320 2 0.1 0.2
321 1 0.1 0.1
324 1 0.1 0.1
325 1 0.1 0.1
330 3 0.2 0.2
335 2 0.1 0.2
340 1 0.1 0.1
360 1 0.1 0.1
365 1 0.1 0.1
400 1 0.1 0.1
50 1 0.1 0.1
60 1 0.1 0.1
65 1 0.1 0.1
7 1 0.1 0.1
71 1 0.1 0.1
78 1 0.1 0.1
80 2 0.1 0.2
84 1 0.1 0.1
92 1 0.1 0.1
97 2 0.1 0.2
98 1 0.1 0.1
NA 242 15.4 NA
Total 1575 100.0 100.0 
  f2kgs <- d[,"f2kgs"]
  new.d <- data.frame(new.d, f2kgs)
  new.d <- apply_labels(new.d, f2kgs = "weight in lbs")
  temp.d <- data.frame (new.d, f2kgs)  
  result<-questionr::freq(temp.d$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
0 9 0.6 29.0
1 2 0.1 6.5
1 7 1 0.1 3.2
110 1 0.1 3.2
111 1 0.1 3.2
155 1 0.1 3.2
175 1 0.1 3.2
2 2 0.1 6.5
22 1 0.1 3.2
23 1 0.1 3.2
3 1 0.1 3.2
37 1 0.1 3.2
45 1 0.1 3.2
5 1 0.1 3.2
50 2 0.1 6.5
65 1 0.1 3.2
7 1 0.1 3.2
76 1 0.1 3.2
90 1 0.1 3.2
91 1 0.1 3.2
NA 1544 98.0 NA
Total 1575 100.0 100.0

F3: Exercise frequency

  • F3. How many days per week do you typically get moderate or strenuous exercise (such as heavy lifting, shop work, construction or farm work, home repair, gardening, bowling, golf, jogging, basketball, riding a bike, etc.)?
    • 4=5-7 times per week
    • 3=3-4 times per week
    • 2=1-2 times per week
    • 1=Less than once per week/do not exercise
  f3 <- as.factor(d[,"f3"])
# Make "*" to NA
f3[which(f3=="*")]<-"NA"
  levels(f3) <- list(Per_week_5_7="4",
                     Per_week_3_4="3",
                     Per_week_1_2="2",
                     Per_week_less_1="1")
  f3 <- ordered(f3, c("Per_week_5_7","Per_week_3_4","Per_week_1_2","Per_week_less_1"))
  
  new.d <- data.frame(new.d, f3)
  new.d <- apply_labels(new.d, f3 = "exercise")
  temp.d <- data.frame (new.d, f3)  
  
  result<-questionr::freq(temp.d$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 206 13.1 14.4 13.1 14.4
Per_week_3_4 412 26.2 28.9 39.2 43.3
Per_week_1_2 425 27.0 29.8 66.2 73.0
Per_week_less_1 385 24.4 27.0 90.7 100.0
NA 147 9.3 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

F4: Minutes of exercise

  • F4. On those days that you do moderate or strenuous exercise, how many minutes did you typically exercise at this level?
    • 2=Less than 30 minutes
    • 3=30 minutes – 1 hour
    • 4=More than 1 hour
    • 1=Do not exercise
  f4 <- as.factor(d[,"f4"])
# Make "*" to NA
f4[which(f4=="*")]<-"NA"
  levels(f4) <- list(Less_than_30_min="2",
                     Between_30_min_1_hour="3",
                     More_than_1_hour="4",
                     Do_not_exercise="1")
  f4 <- ordered(f4, c("Less_than_30_min","Between_30_min_1_hour","More_than_1_hour","Do_not_exercise"))
  
  new.d <- data.frame(new.d, f4)
  new.d <- apply_labels(new.d, f4 = "how many minutes exercise")
  temp.d <- data.frame (new.d, f4)  
  
  result<-questionr::freq(temp.d$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F4")
F4
n % val% %cum val%cum
Less_than_30_min 258 16.4 18.1 16.4 18.1
Between_30_min_1_hour 570 36.2 39.9 52.6 58.0
More_than_1_hour 303 19.2 21.2 71.8 79.2
Do_not_exercise 297 18.9 20.8 90.7 100.0
NA 147 9.3 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

F5: Drink alcohol frequency

  • F5. In the past month, about how often do you have at least one drink of any alcoholic beverage such as beer, wine, a malt beverage, or liquor? One drink is equivalent to a 12 oz beer, a 5 oz glass of wine, or a drink with one shot of liquor.
    • 6=Everyday
    • 5=5-6 times per week
    • 4=3-4 times per week
    • 3=1-2 times per week
    • 2=Fewer than once per week
    • 1=Did not drink
  f5 <- as.factor(d[,"f5"])
# Make "*" to NA
f5[which(f5=="*")]<-"NA"
  levels(f5) <- list(Everyday="6",
                     Per_week_5_6_times="5",
                     Per_week_3_4_times="4",
                     Per_week_1_2_times="3",
                     Per_week_fewer_once="2",
                     Not_drink="1")
  f5 <- ordered(f5, c("Everyday","Per_week_5_6_times","Per_week_3_4_times","Per_week_1_2_times","Per_week_fewer_once","Not_drink"))
  
  new.d <- data.frame(new.d, f5)
  new.d <- apply_labels(new.d, f5 = "how often drink")
  temp.d <- data.frame (new.d, f5)  
  
  result<-questionr::freq(temp.d$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Everyday 68 4.3 4.5 4.3 4.5
Per_week_5_6_times 51 3.2 3.3 7.6 7.8
Per_week_3_4_times 161 10.2 10.6 17.8 18.4
Per_week_1_2_times 215 13.7 14.1 31.4 32.5
Per_week_fewer_once 291 18.5 19.1 49.9 51.6
Not_drink 738 46.9 48.4 96.8 100.0
NA 51 3.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

F6: How many drinks

  • F6. When you drank during the past month, how many drinks do you have on a typical occasion?
    • 3=3 or more drinks
    • 2=1-2 drinks
    • 1=Did not drink
  f6 <- as.factor(d[,"f6"])
# Make "*" to NA
f6[which(f6=="*")]<-"NA"
  levels(f6) <- list(Three_or_more="3",
                     One_to_two_drinks="2",
                     Not_drink="1")
  f6 <- ordered(f6, c("Three_or_more","One_to_two_drinks","Not_drink"))
  
  new.d <- data.frame(new.d, f6)
  new.d <- apply_labels(new.d, f6 = "how many drinks")
  temp.d <- data.frame (new.d, f6)  
  
  result<-questionr::freq(temp.d$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 130 8.3 8.7 8.3 8.7
One_to_two_drinks 619 39.3 41.5 47.6 50.2
Not_drink 742 47.1 49.8 94.7 100.0
NA 84 5.3 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

F7: Smoking history

  • F7. Have you ever smoked at least 100 cigarettes in your lifetime?
    • 1=No
    • 2=Yes
  • F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
    • 555 = “Less than 10”
    • 777 = “75+”
  • F7a. How many cigarettes do you (or did you) usually smoke per day?
    • 1=1-5
    • 2=6-10
    • 3=11-20
    • 4=21-30
    • 5=31+
  • F7b. Have you quit smoking?
    • 1=No
    • 2=Yes
  • F7BAge. If yes, At what age did you quit?
    • 555 = “Less than 10”
    • 777 = “75+”
  f7 <- as.factor(d[,"f7"])
# Make "*" to NA
f7[which(f7=="*")]<-"NA"
  levels(f7) <- list(Yes="2",
                     No="1")
  f7 <- ordered(f7, c("No","Yes"))
  
  new.d <- data.frame(new.d, f7)
  new.d <- apply_labels(new.d, f7 = "smoke")
  temp.d <- data.frame (new.d, f7)  
  
  result<-questionr::freq(temp.d$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 801 50.9 54.6 50.9 54.6
Yes 667 42.3 45.4 93.2 100.0
NA 107 6.8 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  f7age <- d[,"f7age"]
  f7age[which(f7age=="555")]<-"Less_than_10"
  f7age[which(f7age=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7age)
  new.d <- apply_labels(new.d, f7age = "age start to smoke")
  temp.d <- data.frame (new.d, f7age)  
  
  result<-questionr::freq(temp.d$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
0 3 0.2 0.6
1 1 0.1 0.2
10 6 0.4 1.1
11 5 0.3 0.9
12 7 0.4 1.3
13 15 1.0 2.8
14 16 1.0 3.0
15 54 3.4 10.1
16 57 3.6 10.7
17 53 3.4 9.9
18 89 5.7 16.7
19 44 2.8 8.2
20 51 3.2 9.6
21 24 1.5 4.5
22 14 0.9 2.6
23 14 0.9 2.6
24 6 0.4 1.1
25 19 1.2 3.6
26 4 0.3 0.7
27 6 0.4 1.1
28 2 0.1 0.4
29 2 0.1 0.4
30 15 1.0 2.8
32 2 0.1 0.4
33 1 0.1 0.2
35 5 0.3 0.9
38 2 0.1 0.4
40 1 0.1 0.2
41 1 0.1 0.2
42 1 0.1 0.2
44 1 0.1 0.2
45 1 0.1 0.2
49 2 0.1 0.4
5 1 0.1 0.2
50 3 0.2 0.6
54 1 0.1 0.2
60 1 0.1 0.2
7 2 0.1 0.4
8 1 0.1 0.2
9 1 0.1 0.2
NA 1041 66.1 NA
Total 1575 100.0 100.0 
  f7a <- as.factor(d[,"f7a"])
  # Make "*" to NA
f7a[which(f7a=="*")]<-"NA"
  levels(f7a) <- list(One_to_five="1",
                     Six_to_ten="2",
                     Eleven_to_twenty="3",
                     Twentyone_to_Thirty="4",
                     Older_31="5")
  f7a <- ordered(f7a, c("One_to_five","Six_to_ten","Eleven_to_twenty","Twentyone_to_Thirty","Older_31"))

  new.d <- data.frame(new.d, f7a)
  new.d <- apply_labels(new.d, f7a = "How many cigarettes per day")
  temp.d <- data.frame (new.d, f7a)  
  
  result<-questionr::freq(temp.d$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 263 16.7 38.2 16.7 38.2
Six_to_ten 210 13.3 30.5 30.0 68.7
Eleven_to_twenty 155 9.8 22.5 39.9 91.1
Twentyone_to_Thirty 44 2.8 6.4 42.7 97.5
Older_31 17 1.1 2.5 43.7 100.0
NA 886 56.3 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0 
  f7b <- as.factor(d[,"f7b"])
    # Make "*" to NA
f7b[which(f7b=="*")]<-"NA"
  levels(f7b) <- list(No="1",
                     Yes="2")

  new.d <- data.frame(new.d, f7b)
  new.d <- apply_labels(new.d, f7b = "quit smoking")
  temp.d <- data.frame (new.d, f7b)  
  
  result<-questionr::freq(temp.d$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 155 9.8 21.4
Yes 568 36.1 78.6
NA 852 54.1 NA
Total 1575 100.0 100.0 
  f7bage <- d[,"f7bage"]
  f7bage[which(f7bage=="555")]<-"Less_than_10"
  f7bage[which(f7bage=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7bage)
  new.d <- apply_labels(new.d, f7bage = "age quit smoking")
  temp.d <- data.frame (new.d, f7bage)  
  
  result<-questionr::freq(temp.d$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
1 1 0.1 0.2
11 1 0.1 0.2
15 2 0.1 0.4
16 1 0.1 0.2
17 3 0.2 0.6
18 2 0.1 0.4
19 7 0.4 1.3
20 10 0.6 1.9
21 6 0.4 1.1
22 10 0.6 1.9
23 14 0.9 2.6
24 6 0.4 1.1
25 13 0.8 2.4
26 7 0.4 1.3
27 7 0.4 1.3
28 12 0.8 2.2
29 10 0.6 1.9
30 21 1.3 3.9
31 7 0.4 1.3
32 6 0.4 1.1
33 8 0.5 1.5
34 7 0.4 1.3
35 37 2.3 6.9
36 7 0.4 1.3
37 5 0.3 0.9
38 6 0.4 1.1
39 5 0.3 0.9
4 1 0.1 0.2
40 40 2.5 7.5
41 8 0.5 1.5
42 9 0.6 1.7
43 7 0.4 1.3
44 3 0.2 0.6
45 23 1.5 4.3
46 6 0.4 1.1
47 5 0.3 0.9
48 11 0.7 2.1
49 8 0.5 1.5
50 38 2.4 7.1
51 10 0.6 1.9
52 7 0.4 1.3
53 4 0.3 0.7
54 6 0.4 1.1
55 9 0.6 1.7
56 8 0.5 1.5
57 7 0.4 1.3
58 7 0.4 1.3
59 9 0.6 1.7
60 16 1.0 3.0
61 6 0.4 1.1
62 5 0.3 0.9
63 7 0.4 1.3
64 3 0.2 0.6
65 15 1.0 2.8
66 8 0.5 1.5
67 7 0.4 1.3
68 7 0.4 1.3
69 2 0.1 0.4
70 5 0.3 0.9
71 1 0.1 0.2
72 1 0.1 0.2
73 2 0.1 0.4
74 1 0.1 0.2
76 2 0.1 0.4
8 1 0.1 0.2
NA 1039 66.0 NA
Total 1575 100.0 100.0

G1: Marital status

  • G1. What is your current marital status?
    • 1=Married, or living with a partner
    • 2=Separated
    • 3=Divorced
    • 4=Widowed
    • 5=Never Married
  g1 <- as.factor(d[,"g1"])
  # Make "*" to NA
g1[which(g1=="*")]<-"NA"
  levels(g1) <- list(Married_partner="1",
                     Separated="2",
                     Divorced="3",
                     Widowed="4",
                     Never_Married="5")
  g1 <- ordered(g1, c("Married_partner","Separated","Divorced","Widowed","Never_Married"))
  
  new.d <- data.frame(new.d, g1)
  new.d <- apply_labels(new.d, g1 = "marital status")
  temp.d <- data.frame (new.d, g1)  
  
  result<-questionr::freq(temp.d$g1,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val% %cum val%cum
Married_partner 1061 67.4 69.6 67.4 69.6
Separated 49 3.1 3.2 70.5 72.8
Divorced 195 12.4 12.8 82.9 85.6
Widowed 107 6.8 7.0 89.7 92.7
Never_Married 112 7.1 7.3 96.8 100.0
NA 51 3.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

G2: With whom do you live

  • G2. With whom do you live? Mark all that apply.
    • G2_1: 1=Live alone
    • G2_2: 1=A spouse or partner
    • G2_3: 1=Other family
    • G2_4: 1=Other people (non-family)
    • G2_5: 1=Pets
  g2_1 <- as.factor(d[,"g2_1"])
  levels(g2_1) <- list(Live_alone="1")

  new.d <- data.frame(new.d, g2_1)
  new.d <- apply_labels(new.d, g2_1 = "Live alone")
  temp.d <- data.frame (new.d, g2_1)  
  
  result<-questionr::freq(temp.d$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 298 18.9 100
NA 1277 81.1 NA
Total 1575 100.0 100 
  g2_2 <- as.factor(d[,"g2_2"])
  levels(g2_2) <- list(spouse_partner="1")

  new.d <- data.frame(new.d, g2_2)
  new.d <- apply_labels(new.d, g2_2 = "A spouse or partner")
  temp.d <- data.frame (new.d, g2_2)  
  
  result<-questionr::freq(temp.d$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 1071 68 100
NA 504 32 NA
Total 1575 100 100 
  g2_3 <- as.factor(d[,"g2_3"])
  levels(g2_3) <- list(Other_family="1")

  new.d <- data.frame(new.d, g2_3)
  new.d <- apply_labels(new.d, g2_3 = "Other family")
  temp.d <- data.frame (new.d, g2_3)  
  
  result<-questionr::freq(temp.d$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 161 10.2 100
NA 1414 89.8 NA
Total 1575 100.0 100 
  g2_4 <- as.factor(d[,"g2_4"])
  levels(g2_4) <- list(Other_non_family="1")

  new.d <- data.frame(new.d, g2_4)
  new.d <- apply_labels(new.d, g2_4 = "Other people (non-family)")
  temp.d <- data.frame (new.d, g2_4)  
  
  result<-questionr::freq(temp.d$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 28 1.8 100
NA 1547 98.2 NA
Total 1575 100.0 100 
  g2_5 <- as.factor(d[,"g2_5"])
  levels(g2_5) <- list(Pets="1")

  new.d <- data.frame(new.d, g2_5)
  new.d <- apply_labels(new.d, g2_5 = "Pets")
  temp.d <- data.frame (new.d, g2_5)  
  
  result<-questionr::freq(temp.d$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 78 5 100
NA 1497 95 NA
Total 1575 100 100

G3: Identify yourself

  • G3. How do you identify yourself?
    • 1=Straight/heterosexual
    • 2=Bisexual
    • 3=Gay/homosexual/same gender loving
    • 4=Other
    • 99=Prefer not to answer
  g3 <- as.factor(d[,"g3"])
  # Make "*" to NA
g3[which(g3=="*")]<-"NA"
  levels(g3) <- list(heterosexual="1",
                      Bisexual="2",
                       homosexual="3",
                       Other="4",
                       Prefer_not_to_answer="99")
  g3 <- ordered(g3, c("heterosexual","Bisexual","homosexual","Other","Prefer_not_to_answer"))

  new.d <- data.frame(new.d, g3)
  new.d <- apply_labels(new.d, g3 = "identify yourself")
  temp.d <- data.frame (new.d, g3)  
  
  result<-questionr::freq(temp.d$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 1432 90.9 95.9
Bisexual 7 0.4 0.5
homosexual 10 0.6 0.7
Other 11 0.7 0.7
Prefer_not_to_answer 34 2.2 2.3
NA 81 5.1 NA
Total 1575 100.0 100.0

G3 Other: Identify yourself

g3other <- d[,"g3other"]
  new.d <- data.frame(new.d, g3other)
  new.d <- apply_labels(new.d, g3other = "g3other")
  temp.d <- data.frame (new.d, g3other)
result<-questionr::freq(temp.d$g3other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G3 Other")
G3 Other
n % val%
A man 2 0.1 6.9
All man by the grace of God 1 0.1 3.4
Christian. 1 0.1 3.4
Do not want to be confused like women nothing else 1 0.1 3.4
Get hard, I wish it would. 1 0.1 3.4
Human 1 0.1 3.4
Human! 1 0.1 3.4
I am man married to woman 1 0.1 3.4
Just straight 1 0.1 3.4
Male 2 0.1 6.9
Male/Married 1 0.1 3.4
Man 2 0.1 6.9
No 1 0.1 3.4
None heterosexual 1 0.1 3.4
Normal human being 1 0.1 3.4
Self 1 0.1 3.4
Straight 6 0.4 20.7
Straight male 2 0.1 6.9
Straight man 1 0.1 3.4
What God made me a man 1 0.1 3.4
NA 1546 98.2 NA
Total 1575 100.0 100.0

G4: Education

  • G4. What is the HIGHEST level of education you, your father, and your mother have completed?
    • 1=Grade school or less
    • 2=Some high school
    • 3=High school graduate or GED
    • 4=Vocational school
    • 5=Some college
    • 6=Associate’s degree
    • 7=College graduate (Bachelor’s degree)
    • 8=Some graduate education
    • 9=Graduate degree
    • 88=Don’t know
  g4a <- as.factor(d[,"g4a"])
  # Make "*" to NA
g4a[which(g4a=="*")]<-"NA"
  levels(g4a) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9")
  g4a <- ordered(g4a, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree"))

  new.d <- data.frame(new.d, g4a)
  new.d <- apply_labels(new.d, g4a = "education")
  temp.d <- data.frame (new.d, g4a)  
  
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 51 3.2 3.7
Some_high_school 128 8.1 9.2
High_school_graduate_GED 400 25.4 28.7
Vocational_school 75 4.8 5.4
Some_college 269 17.1 19.3
Associate_degree 112 7.1 8.0
College_graduate 165 10.5 11.8
Some_graduate_education 46 2.9 3.3
Graduate_degree 147 9.3 10.6
NA 182 11.6 NA
Total 1575 100.0 100.0 
  g4b <- as.factor(d[,"g4b"])
    # Make "*" to NA
g4b[which(g4b=="*")]<-"NA"
  levels(g4b) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88")
  g4b <- ordered(g4b, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))

  new.d <- data.frame(new.d, g4b)
  new.d <- apply_labels(new.d, g4b = "education-father")
  temp.d <- data.frame (new.d, g4b)  
  
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 423 26.9 31.0
Some_high_school 210 13.3 15.4
High_school_graduate_GED 266 16.9 19.5
Vocational_school 33 2.1 2.4
Some_college 34 2.2 2.5
Associate_degree 12 0.8 0.9
College_graduate 32 2.0 2.3
Some_graduate_education 4 0.3 0.3
Graduate_degree 20 1.3 1.5
Dont_know 332 21.1 24.3
NA 209 13.3 NA
Total 1575 100.0 100.0 
  g4c <- as.factor(d[,"g4c"])
    # Make "*" to NA
g4c[which(g4c=="*")]<-"NA"
  levels(g4c) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88")
  g4c <- ordered(g4c, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))

  new.d <- data.frame(new.d, g4c)
  new.d <- apply_labels(new.d, g4c = "education-mother")
  temp.d <- data.frame (new.d, g4c)  
  
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 301 19.1 21.8
Some_high_school 250 15.9 18.1
High_school_graduate_GED 376 23.9 27.3
Vocational_school 52 3.3 3.8
Some_college 45 2.9 3.3
Associate_degree 23 1.5 1.7
College_graduate 51 3.2 3.7
Some_graduate_education 4 0.3 0.3
Graduate_degree 34 2.2 2.5
Dont_know 242 15.4 17.6
NA 197 12.5 NA
Total 1575 100.0 100.0

G5: Job

  • G5. Which one of the following best describes what you currently do?
    • 1=Currently working full-time
    • 2=Currently working part-time
    • 3=Looking for work, unemployed
    • 4=Retired
    • 5=On disability permanently
    • 6=On disability for a period of time (on sick leave or paternity leave or disability leave for other reasons)
    • 7=Volunteer work/work without pay
    • 8=Other
  g5 <- as.factor(d[,"g5"])
  # Make "*" to NA
g5[which(g5=="*")]<-"NA"
  levels(g5) <- list(full_time="1",
                     part_time="2",
                     unemployed="3",
                     Retired="4",
                     disability_permanently="5",
                     disability_for_a_time="6",
                     Volunteer_work="7",
                     Other="8")
  g5 <- ordered(g5, c("full_time","part_time","unemployed","Retired","disability_permanently","disability_for_a_time", "Volunteer_work","Other"))

  new.d <- data.frame(new.d, g5)
  new.d <- apply_labels(new.d, g5 = "job")
  temp.d <- data.frame (new.d, g5)  
  
  result<-questionr::freq(temp.d$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 361 22.9 25.1
part_time 77 4.9 5.4
unemployed 16 1.0 1.1
Retired 753 47.8 52.4
disability_permanently 202 12.8 14.1
disability_for_a_time 12 0.8 0.8
Volunteer_work 4 0.3 0.3
Other 11 0.7 0.8
NA 139 8.8 NA
Total 1575 100.0 100.0

G5 Other: job

g5other <- d[,"g5other"]
  new.d <- data.frame(new.d, g5other)
  new.d <- apply_labels(new.d, g5other = "g5other")
  temp.d <- data.frame (new.d, g5other)
result<-questionr::freq(temp.d$g5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G5 Other")
G5 Other
n % val%
60% disable veteran 1 0.1 2.1
Currently laid off, because of Covid 19. 1 0.1 2.1
Cut grass and pick up cans. 1 0.1 2.1
Disability retirement 1 0.1 2.1
Disabled from my deadly double 18 wheeler truck. The truck driver died. 1 0.1 2.1
Disabled vet/retired from DHS 1 0.1 2.1
Elected official-city council 1 0.1 2.1
I am living off Gov. income —-. 1 0.1 2.1
I still work sometimes. 1 0.1 2.1
I’m a lawyer for 44 years. 1 0.1 2.1
In business. 1 0.1 2.1
Lack of work because of Covid 19 1 0.1 2.1
Lawn and lawn mower repair occasionally 1 0.1 2.1
Medically retired due to surgery got to have more surgery. 1 0.1 2.1
Minister 1 0.1 2.1
Minister, author, poet 1 0.1 2.1
Need to be on disability permanently 1 0.1 2.1
Not working due to Covid 19 pandemic 1 0.1 2.1
Now receiving Social Security benefits 1 0.1 2.1
On disability, for another year, had kidney transplant 1 0.1 2.1
Out on sick leave, after Liphoma removal. 1 0.1 2.1
Own my own business. 1 0.1 2.1
Own PC home 1 0.1 2.1
Own vending business 1 0.1 2.1
Part time peel around 1 0.1 2.1
Pastor. 1 0.1 2.1
Prefer not to answer. 1 0.1 2.1
Retired 3 0.2 6.2
Retired but doing some work (acting) 1 0.1 2.1
Retired military 2 0.1 4.2
Retired military and public education and now pastor of a CME church. 1 0.1 2.1
Retired military working full time security 1 0.1 2.1
retired part time work 1 0.1 2.1
Retired, but work on our farm, some construction 1 0.1 2.1
Self 1 0.1 2.1
Self employed 2 0.1 4.2
Small business owner 1 0.1 2.1
Social security 1 0.1 2.1
Teach tennis free to kids 1 0.1 2.1
Unemployed 1 0.1 2.1
Varies 1 0.1 2.1
Work full with disability aka left 1 0.1 2.1
yard work 1 0.1 2.1
Yard work. 1 0.1 2.1
NA 1527 97.0 NA
Total 1575 100.0 100.0

G6: Health insurance

  • G6. What kind of health insurance or health care coverage do you currently have? Mark all that apply.
    • G6_1: 1=Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
    • G6_2: 1=Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
    • G6_3: 1=Insurance purchased directly from an insurance company (by you or another family member)
    • G6_4: 1=Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
    • G6_5: 1= Medicaid or other state provided insurance
    • G6_6: 1=Medicare/government insurance
    • G6_7: 1=VA/Military Facility (including those who have ever used or enrolled for VA health care)
    • G6_8: 1=I do not have any medical insurance
  g6_1 <- as.factor(d[,"g6_1"])
  levels(g6_1) <- list(Insurance_employer="1")
  new.d <- data.frame(new.d, g6_1)
  new.d <- apply_labels(new.d, g6_1 = "Insurance_employer")
  temp.d <- data.frame (new.d, g6_1)  
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 450 28.6 100
NA 1125 71.4 NA
Total 1575 100.0 100 
  g6_2 <- as.factor(d[,"g6_2"])
  levels(g6_2) <- list(Insurance_family="1")
  new.d <- data.frame(new.d, g6_2)
  new.d <- apply_labels(new.d, g6_2 = "Insurance_family")
  temp.d <- data.frame (new.d, g6_2)  
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 130 8.3 100
NA 1445 91.7 NA
Total 1575 100.0 100 
  g6_3 <- as.factor(d[,"g6_3"])
  levels(g6_3) <- list(Insurance_insurance_company="1")
  new.d <- data.frame(new.d, g6_3)
  new.d <- apply_labels(new.d, g6_3 = "Insurance_insurance_company")
  temp.d <- data.frame (new.d, g6_3)  
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_3. Insurance purchased directly from an insurance company (by you or another family member)")
G6_3. Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 110 7 100
NA 1465 93 NA
Total 1575 100 100 
  g6_4 <- as.factor(d[,"g6_4"])
  levels(g6_4) <- list(Insurance_exchange="1")
  new.d <- data.frame(new.d, g6_4)
  new.d <- apply_labels(new.d, g6_4 = "Insurance_exchange")
  temp.d <- data.frame (new.d, g6_4)  
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 45 2.9 100
NA 1530 97.1 NA
Total 1575 100.0 100 
  g6_5 <- as.factor(d[,"g6_5"])
  levels(g6_5) <- list(Medicaid_state="1")
  new.d <- data.frame(new.d, g6_5)
  new.d <- apply_labels(new.d, g6_5 = "Medicaid_state")
  temp.d <- data.frame (new.d, g6_5)  
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_5. Medicaid or other state provided insurance")
G6_5. Medicaid or other state provided insurance
n % val%
Medicaid_state 217 13.8 100
NA 1358 86.2 NA
Total 1575 100.0 100 
  g6_6 <- as.factor(d[,"g6_6"])
  levels(g6_6) <- list(Medicare_government="1")
  new.d <- data.frame(new.d, g6_6)
  new.d <- apply_labels(new.d, g6_6 = "Medicare_government")
  temp.d <- data.frame (new.d, g6_6)  
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_6. Medicare/government insurance")
G6_6. Medicare/government insurance
n % val%
Medicare_government 805 51.1 100
NA 770 48.9 NA
Total 1575 100.0 100 
  g6_7 <- as.factor(d[,"g6_7"])
  levels(g6_7) <- list(VA_Military="1")
  new.d <- data.frame(new.d, g6_7)
  new.d <- apply_labels(new.d, g6_7 = "VA_Military")
  temp.d <- data.frame (new.d, g6_7)  
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)")
G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 352 22.3 100
NA 1223 77.7 NA
Total 1575 100.0 100 
  g6_8 <- as.factor(d[,"g6_8"])
  levels(g6_8) <- list(Do_not_have="1")
  new.d <- data.frame(new.d, g6_8)
  new.d <- apply_labels(new.d, g6_8 = "Do_not_have")
  temp.d <- data.frame (new.d, g6_8)  
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_8. I do not have any medical insurance")
G6_8. I do not have any medical insurance
n % val%
Do_not_have 34 2.2 100
NA 1541 97.8 NA
Total 1575 100.0 100

G7: Income

  • G7. What is your best estimate of your TOTAL FAMILY INCOME from all sources, before taxes, in the last calendar year? “Total family income” refers to your income PLUS the income of all family members living in this household (including cohabiting partners, and armed forces members living at home). This includes money from pay checks, government benefit programs, child support, social security, retirement funds, unemployment benefits, and disability.
    • 1=Less than $15,000
    • 2=$15,000 to $35,999
    • 3=$36,000 to $45,999
    • 4=$46,000 to $65,999
    • 5=$66,000 to $99,999
    • 6=$100,000 to $149,999
    • 7=$150,000 to $199,999
    • 8= $200,000 or more
  g7 <- as.factor(d[,"g7"])
  # Make "*" to NA
g7[which(g7=="*")]<-"NA"
  levels(g7) <- list(Less_than_15000="1",
                     Between_15000_35999="2",
                     Between_36000_45999="3",
                     Between_46000_65999="4",
                     Between_66000_99999="5",
                     Between_100000_149999= "6",
                     Between_150000_199999="7",
                     More_than_200000="8")
  g7 <- ordered(g7, c("Less_than_15000","Between_15000_35999","Between_36000_45999","Between_46000_65999","Between_66000_99999","Between_100000_149999", "Between_150000_199999","More_than_200000"))

  new.d <- data.frame(new.d, g7)
  new.d <- apply_labels(new.d, g7 = "income")
  temp.d <- data.frame (new.d, g7)  
  
  result<-questionr::freq(temp.d$g7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g7")
g7
n % val% %cum val%cum
Less_than_15000 205 13.0 14.3 13.0 14.3
Between_15000_35999 328 20.8 22.9 33.8 37.2
Between_36000_45999 163 10.3 11.4 44.2 48.6
Between_46000_65999 241 15.3 16.8 59.5 65.4
Between_66000_99999 237 15.0 16.5 74.5 81.9
Between_100000_149999 160 10.2 11.2 84.7 93.1
Between_150000_199999 68 4.3 4.7 89.0 97.8
More_than_200000 31 2.0 2.2 91.0 100.0
NA 142 9.0 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

G8: # people supported by income

  • G8. In the last calendar year, how many people, including yourself, were supported by your family income?
    • 1=1
    • 2=2
    • 3=3
    • 4=4
    • 5=5 or more
  g8 <- as.factor(d[,"g8"])
  # Make "*" to NA
g8[which(g8=="*")]<-"NA"
  levels(g8) <- list(One="1",
                     Two="2",
                     Three="3",
                     Four="4",
                     Five_or_more="5")
  g8 <- ordered(g8, c("One","Two","Three","Four","Five_or_more"))

  new.d <- data.frame(new.d, g8)
  new.d <- apply_labels(new.d, g8 = "people supported by income")
  temp.d <- data.frame (new.d, g8)  
  
  result<-questionr::freq(temp.d$g8,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g8")
g8
n % val% %cum val%cum
One 401 25.5 27.1 25.5 27.1
Two 707 44.9 47.8 70.3 75.0
Three 217 13.8 14.7 84.1 89.6
Four 96 6.1 6.5 90.2 96.1
Five_or_more 57 3.6 3.9 93.8 100.0
NA 97 6.2 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

G9: Worry about finance

  • G9. How worried were you or your family about being able to pay your normal monthly bills, including rent, mortgage, and/or other costs:
      1. During young adult life (up to age 30):
      1. Age 31 (up to just before prostate cancer diagnosis):
      1. Current (from prostate cancer diagnosis to present):
      • 1=Not at all worried
      • 2=A little worried
      • 3=Somewhat worried
      • 4=Very worried
  g9a <- as.factor(d[,"g9a"])
  # Make "*" to NA
g9a[which(g9a=="*")]<-"NA"
  levels(g9a) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9a)
  new.d <- apply_labels(new.d, g9a = "young adult life")
  temp.d <- data.frame (new.d, g9a)  
  result<-questionr::freq(temp.d$g9a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. During young adult life (up to age 30)")
a. During young adult life (up to age 30)
n % val%
Not_worried 743 47.2 49.3
A_little_worried 394 25.0 26.2
Somewhat_worried 251 15.9 16.7
Very_worried 118 7.5 7.8
NA 69 4.4 NA
Total 1575 100.0 100.0 
  g9b <- as.factor(d[,"g9b"])
    # Make "*" to NA
g9b[which(g9b=="*")]<-"NA"
  levels(g9b) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9b)
  new.d <- apply_labels(new.d, g9b = "age 31 up to before dx")
  temp.d <- data.frame (new.d, g9b)  
  result<-questionr::freq(temp.d$g9b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Age 31 (up to just before prostate cancer diagnosis)")
b. Age 31 (up to just before prostate cancer diagnosis)
n % val%
Not_worried 735 46.7 50.9
A_little_worried 407 25.8 28.2
Somewhat_worried 204 13.0 14.1
Very_worried 98 6.2 6.8
NA 131 8.3 NA
Total 1575 100.0 100.0 
  g9c <- as.factor(d[,"g9c"])
    # Make "*" to NA
g9c[which(g9c=="*")]<-"NA"
  levels(g9c) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9c)
  new.d <- apply_labels(new.d, g9c = "current")
  temp.d <- data.frame (new.d, g9c)  
  result<-questionr::freq(temp.d$g9c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Current (from prostate cancer diagnosis to present)")
c. Current (from prostate cancer diagnosis to present)
n % val%
Not_worried 824 52.3 56.1
A_little_worried 300 19.0 20.4
Somewhat_worried 206 13.1 14.0
Very_worried 140 8.9 9.5
NA 105 6.7 NA
Total 1575 100.0 100.0

G10:Own or rent a house

  • G10. Is the home you live in:
    • 1=Owned or being bought by you (or someone in the household)?
    • 2=Rented for money?
    • 3=Other
  g10 <- as.factor(d[,"g10"])
  # Make "*" to NA
g10[which(g10=="*")]<-"NA"
  levels(g10) <- list(Owned="1",
                     Rented="2",
                     Other="3")
  g10 <- ordered(g10, c("Owned","Rented","Other"))

  new.d <- data.frame(new.d, g10)
  new.d <- apply_labels(new.d, g10 = "Own or rent a house")
  temp.d <- data.frame (new.d, g10)  
  
  result<-questionr::freq(temp.d$g10,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g10")
g10
n % val% %cum val%cum
Owned 1220 77.5 81.1 77.5 81.1
Rented 251 15.9 16.7 93.4 97.8
Other 33 2.1 2.2 95.5 100.0
NA 71 4.5 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

G10 Other: Own or rent a house

g10other <- d[,"g10other"]
  new.d <- data.frame(new.d, g10other)
  new.d <- apply_labels(new.d, g10other = "g10other")
  temp.d <- data.frame (new.d, g10other)
result<-questionr::freq(temp.d$g10other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G10 Other")
G10 Other
n % val%
#NAME? 1 0.1 1.8
A friend 1 0.1 1.8
Agency 1 0.1 1.8
Apartment renting 1 0.1 1.8
Assisted living 1 0.1 1.8
Buying 1 0.1 1.8
Cooperative 1 0.1 1.8
Currently live in a trailer on family land 1 0.1 1.8
Daughter 1 0.1 1.8
Estate 1 0.1 1.8
Family 1 0.1 1.8
Family property 1 0.1 1.8
House belongs to my wife, Mrs. —- this home is permanent. Thank God. 1 0.1 1.8
I currently rent an apartment 1 0.1 1.8
I have a room in this house 1 0.1 1.8
I live with my mother in her home. 1 0.1 1.8
I pay rent 1 0.1 1.8
I rent 2 0.1 3.5
I rent my house from owner. 1 0.1 1.8
I rent the home 1 0.1 1.8
If you need to call me Larry D. Joyner 912-344-5895. 1 0.1 1.8
Live assisted living 1 0.1 1.8
Live in apartment community 1 0.1 1.8
Living with a family member. 1 0.1 1.8
Living with cousin’s home 1 0.1 1.8
Mortgage 2 0.1 3.5
My sisters home 1 0.1 1.8
Own 1 0.1 1.8
Owned 2 0.1 3.5
Owned —- out to 1985, zero love dollars and affection 1 0.1 1.8
Owned by parent, paying rent, car note 1 0.1 1.8
Owned by someone in household. 1 0.1 1.8
Owned by Step daughter 1 0.1 1.8
Owned. 1 0.1 1.8
Passed down to me 1 0.1 1.8
Pay for! 1 0.1 1.8
Paying mortgage 1 0.1 1.8
Paying mortgage. 1 0.1 1.8
Paying rent 1 0.1 1.8
Rent 3 0.2 5.3
Rent I pay 200 month 1 0.1 1.8
Rent room 1 0.1 1.8
Rental 1 0.1 1.8
Rented home 1 0.1 1.8
Renting a home 1 0.1 1.8
Reverse mortgage 1 0.1 1.8
Rooming 1 0.1 1.8
Son owns the home-I pay the mortgage. 1 0.1 1.8
Staying with relative who owns the home 1 0.1 1.8
Wife 2 0.1 3.5
Work compensation 1 0.1 1.8
NA 1518 96.4 NA
Total 1575 100.0 100.0

G11:Lose current sources

  • G11. If you lost all your current source(s) of household income (your paycheck, public assistance, or other forms of income), how long could you continue to live at your current address and standard of living?
    • 1=Less than 1 month
    • 2=1 to 2 months
    • 3=3 to 6 months
    • 4=More than 6 months
  g11 <- as.factor(d[,"g11"])
  # Make "*" to NA
g11[which(g11=="*")]<-"NA"
  levels(g11) <- list(Less_than_1_month="1",
                     One_to_two_month="2",
                     Three_to_six_month="3",
                     More_than_6_months="4")
  g11 <- ordered(g11, c("Less_than_1_month","One_to_two_month","Three_to_six_month","More_than_6_months"))

  new.d <- data.frame(new.d, g11)
  new.d <- apply_labels(new.d, g11 = "ose current sources")
  temp.d <- data.frame (new.d, g11)  
  
  result<-questionr::freq(temp.d$g11,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g11")
g11
n % val% %cum val%cum
Less_than_1_month 160 10.2 10.9 10.2 10.9
One_to_two_month 317 20.1 21.6 30.3 32.5
Three_to_six_month 324 20.6 22.1 50.9 54.5
More_than_6_months 668 42.4 45.5 93.3 100.0
NA 106 6.7 NA 100.0 NA
Total 1575 100.0 100.0 100.0 100.0

G12: Today’s date

  • G12. Please enter today’s date.
  g12 <- as.Date(d[ , "g12"], format="%m/%d/%y")
  new.d <- data.frame(new.d, g12)
  new.d <- apply_labels(new.d, g12 = "today’s date")
  #temp.d <- data.frame (new.d.1, g12) 
  
  summarytools::view(dfSummary(new.d$g12, style = 'grid',
                               max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 g12 [labelled, Date] today’s date
min : 1980-08-23
med : 2020-06-21
max : 2020-12-31
range : 40y 4m 8d
 371 distinct values 43 (2.7%)

Generated by summarytools 1.0.0 (R version 3.6.3)
2021-12-09